Determination of satisfaction and desire in virtual creatures

ABSTRACT

There are described methods for determining intensities of satisfactions and desires of a virtual creature vP (e.g. motivated agent system, virtual man in Internet). These intensities are represented, at time t, by functions bef(vP,b,t) (the intensity of satisfaction with respect to need b) and des(vP,b,t) (the intensity of desire with respect to need b). In this paper are described methods for determining intensities bef(vP,b,t) and des(vP,b,t). These methods may be applied in order to: (i) simulate satisfactions and desires, and determine emotion states of an artificial creature vP, (ii) develop a control algorithm which determines the behavior of vP, (iii) build in such semantic of man emotions into artificial creature vP that vP would better understand emotional behavior of a man.

1. Introduction

In this paper are described methods for determining intensities ofsatisfactions and desires (tensions of needs) of virtual creature vP (eg. a motivated agent system, a virtual human in Internet) The state (theintensity) of satisfaction and desire of virtual creature vP, withrespect to need b (at time t), is represented by functions bef(vP,b,t)und des(vP,b,t). These functions were introduced (not formal) and usedin Schurmann [AS4] (1998), [AS3] (1998), [AS1] (2000). The descriptionof the patent application [AS1] (2000) is based on function valuesbef(vP,b,t) and des(vP,b,t). Until now, there is no method fordetermining intensities of satisfaction bef(vP,b,t) and desiredes(vP,b,t) for an artificial creature vP. The methods for determiningintensities bef(vP,b,t) and des(vP,b, t) described in this paper can beapplied in order.

i. to simulate satisfactions and desires of an artificial creature vP (eg a motivated agent system, a virtual human or mammal in Internet orentertainment software) On the basis of patent description [AS1] (2000),emotion states of vP may with that be formal represented;

ii. to develop motivation function and control algorithm (e.g. asdescribed in Schurnann [AS4] (1998), Sect. 2.7), for artificial creaturevP, which determine the behaviour of vP;

iii. to build in such semantic of emotions into creature vP that vPwould better understand human emotions.

The methods presented here use notions, functions and methods describedin Schurmann [AS1] (2000). These notions and functions are presented inshort in Sect. 2 of this paper. In Sect. 3 is given a method fordetermining intensities of satisfaction, bef(vP,b,t), and desire,des(vP,b,t), by stimulus patterns in situation models and activitydescriptions, for needs which are individually associated withsituations or activities: GE (to be healthy), AN (for recognition,acknowledgment and self-esteem), LE (to be alive), KS (to have no pain),SE (for sexual relations), NU (to be in normal environment), BW (forbodily activities), SN (for tasty food), SH (for visual beauty). Thegreatest part of this paper (Sect. 4) contains descriptions of methodsfor determining intensities bef(vP,b,t) and des(vP,b,t) for standardsituations of vP, for the following needs: AU (for attention andidentification), NE (curiosity and the need for knowledge), GR (tobelong to communities), MA (to have power over people and animals), LI(for liking and love), MB (material and financial needs), BZ(Sz) (toachieve goal situation Sz), NA (to have children), BH(OK) (to helpobject OK).

2. Notations and Functions used in my Patent Description [AS1]

We use, in this paper, following notions and functions described in mypaper [AS I] (2000), Artificial creature vP has a set Bd(vP) of needs.To Bd(vP) belong e.g. GR (belong to communities), MA (have power), MB(material and financial needs), BZ(Sz) (achieve goal situation Sz).

The state of tension (desire) and satisfaction of vP, with respect toneed b, at time 1, is given by two functions:

0≦des(vP,b,t)≦60, −30≦bef(fP,b,t)≦30, for bεBd(vP)

where des(vP,b,t) is the intensity of desire and bef(vP,b,t) theintensity of satisfaction (or dissatisfaction) of vP, with respect toneed b, at time t. These functions have the following properties.

i. Increasing function bef(vP,b,t) means vP satisfies his/her need b(positive stimulus) and is perceived by vP with approval, joy orhappiness.

ii. When bef(vP,b,t)<0 and does not increase then vP perceivesbef(vP,b,t) as a negative stimulus (disappointment, annoyance, sadness,suffering) with respect to need b. Decreasing bef(vP,b,t)<0 meansstronger negative stimulus with regard to need b.

iii. des(vP,b,t) is the intensity of desire of vP to satisfy need b attime L. The greater des(vP,b,t) the greater is the desire of vP tosatisfy need b. des(vP,b,t)<0.5 means ‘need b of vP is well satisfied attime t’.

iv. The greater des(vP,b,t) the greater is the approval and joy of vPwhen bef(vP,b,t) increases, and the greater is the dissatisfaction,annoyance and grief of vP when bef(vP,b,t)<0 and decreases.

Artificial creature vP has models of objects and situations (OSM) ofhis/her/its surrounding, and models (schemes) of activities (behaviours,operations, procedures) which vP may execute. Object, situation oractivity, OSA, has the same structure as its object, situation oractivity model, respectively. Stimulus patterns. In model OSM (and OSA)stimuli are represented by patterns (called stimulus patterns) of thefollowing form:

(2.1) ([^(∘)|(Nba, Nb),]fs(vP,b)=([^(∘) |p;]n;(y1,z1), . . . ,(yn,zn); qht) [^(∘) |/ z eu][^(∘) |;OSM1.Ej] [^(∘)|; where C])

where [tex1|. . . |texk] denotes one of the words tex1, . . . , texk,^(∘) is the empty word, Nba, Nb and n are natural numbers, NBa≦Nb,1≦n≦10),fs denotes name of a stimulus pattern, 0≦p≦1, −30≦yi≦30,−55≦zi≦60, yi and zi are simple arithmetical expressions, q hi denotes atime period (e g: 0.5 h, 3 days, 1 week), n*q ht≦720 h, z>0, eu denotesa measure (e g kg, g, h, km, m, l) and e g/200 g denotes ‘pro 200 g’.Nba/Nb is the probability that the pattern fs(vP,b)=( . . . ) is valid.C is a condition. If C occurs then [^(∘)|(Nba,Nb),]fs(vP,b)=( . . . )can be applied only if C is true. If OSM1.Ej occurs then the patternfs(vP,b)=( . . . ) concerns the pattern Ej=(‘ds’,( . . . fse(vP,b)=. . .)) in OSM1. Example of a pattern (fs=epb) occurring in OSM:

(2.2) epb(vP,b)=(n;(y1,z1), . . . ,(yn,zn);q ht) [^(∘) |/ z eu]

where yn>1+y1 and z1>1+zn. The meaning: vP can execute (time t) anactivity, AV, such that when vP uses OSM in AV then vP expects that OSMwill increase bef(vP,b,.) and decrease des(vP,b,.) according to thepattern (2.2). Exact description of all patterns and their meanings isgiven in Schurmann [AS1], Sect. 2.2.

Intensity of stimulus. Expected (by vP, at time t) intensity of positivestimulus of pattern (2.1) is given by epr(vP,OSM,fsp,b, . . . ,t)(defined in [AS1], Sect. 2.3.1), where fsp denotes the following(positive) pattern names: epb, upb, vnb, epbu, upbu. Let fsn denotes thefollowing names of (negative) patterns: enb, unb, enbu, unbu, vpb, vnb.Expected (by vP, at time t) intensity of negative stimulus of thepattern (2.1) (where fs=fsn) is given by enr(vP,OSM,fsn,b, . . . ,t)(defined in [AS1], Sect. 2.3.2). The intensity of positive stimulus ofOSM (time t) is given by

pros(vP,OSM,t)=Σ_(bεBp) epr(vP,OSM,fsp,b, . . . ,t)

where Bp={bε WB|( . . . fsp(vP,b)=.) is in OSM}, WB={bεBd(vP)|des(vP,b,t)>0.33* mdes(vP,t) } and mdes(vP,t)=max(des(vP,b,t),for b ε Bd(vP)). The intensity of negative stimulus of OSM (time t) isgiven by:

nros(vP,OSM,t)=Σ_(bεBn) enr(vP,OSM,fsn,b, . . . ,t)

where Bn={bεWB|( . . . fsn(vP,b)=. . . ) is in OSM}A. The intensity ofstimulus of OSM at time t (s. [AS1], Sect. 2.3.3):

rosa(vP,OSM,t)=pros(vP,OSM,t)−nros(vP,OSM,t).

Intensities of feelings. The states of feelings of vP: contentment, joy,happiness, dissatisfaction, annoyance and suffering, with respect toneed b, at time t, are represented by function values zful(vP,b,t) (howthey are determined is described in [AS1], Sect. 3). zful(vP,b,t) isinterpreted as follows:

0≦zful(vP,b,t)—the intensity of contentment (the small values), joy (themiddle values), happiness (the great values) of vP, with regard to needb;

0>zful(vP,b,t)—the intensity of dissatisfaction (the greater values),annoyance, grief, sadness and suffering (the smaller values) of vP withregard to need b.

Intensities of liking, affection, love, dislike, annoyance and angerto/for an object, a situation or an activity (OS4) are given by twofunctions (how they are determined is described in [AS1] Sect. 4):

zulieb(vP,OSA,t)—the intensity of liking, affection and love of vPto/for OSA at time t—the greater this value the stronger is the positivefeeling of vP to OSA;

abhas(vP,OSA,t)—the intensity of dislike, aversion and anger of vPto/for OSA at time t—the greater this value the stronger is the negativefeeling of vP to OSA.

3. Determination of bef(vP,b,t) and des(vP,b,t) by Stimulus Patterns

Artificial creature vP has a set WPI(vP) of perceiving procedures whichidentify objects, situations and activities in the surrounding of vP.WPI(vP) contains procedures for: visual identification of objects andsituations, identification of artificial creatures and real persons bynames and passwords, perceiving objects by touch, syntactic and semanticidentification of clauses. vP sends intensities of desires,satisfactions and emotions, to other artificial creatures, as valuesdes(vP,b,t), bef(vP,b,t), zful(vP,b,t), zulieb(vP,OS4,t),abhas(vP,OSA,t), . . . . To people, vP expresses these values byclauses.

In this section we consider the determination of values bef(vP,b,t) anddes(vP b,t) by stimulus patterns occurring in situation and activitymodels. For needs b in {GE (be healthy), LE (be alive), KS (have nopain), NU (be in normal environment), SH (visual beauty)}, these valuesare determined only by stimulus patterns. When vP perceives an object,Ob, then Ob is a component of a situation, S(Ob), which vP hasperceived. The alteration of bef(vp,b,t) and des(vP,b,t), caused byobject Ob in situation S(Ob), is determined by S(Ob). Therefore, weconsider (in this section) only changes of bef(vP,b,t) and des(vP,b,t)caused by situations and activities. Object and situation models, OS,may contain several stimulus patterns ( . . . fsj(vP,b)=. . . where Cj),for j=1, . . . j1. Cj may have the form in AF1{circumflex over ( )}. . .This means: this pattern is valid only when OS is in activity AF1.

When vP has identified, by procedures in WPI(vP), that he/she/it is innew situation, SMn, which contains stimulus pattern . . . efs(vP,b)= . .. , then values bef(vP,b,t) and des(vP,b,t) are determined by thispattern (as shown below) if its priority is actually high enough, whereefs differs from upb, unb, upbu, unbu. For this purpose, we associate toeach stimulus pattern, in situation or activity model, a priority numberas follows: (pr=′r, (Nba, Nb), s(vP,b)=. . . ), where r equals 1 or 2and determines the priority of this stimulus pattern.

3.1. Determination of bef(vP,b,t) and des(vP,b,t) when vP has perceiveda situation

Case: In situation model SMn is, for need b, only one pattern of theform

(3.1) (pr=′r, (Nba, Nb), efs(vP,b)=(n;(y1,z1), . . . , (yn, zn); q ht)[^(∘)|/ z eu [^(∘)|; where C]) valid (i e condition C is true) at timet, where efs denotes epb, epbu, enb, enbu. If r=1 then pattern (3.1) isapplied, with probability Nba/Nb, to calculation of valuesbef(vP,b,t+i*q ht) and des(vP,b,t+i*q ht) (i=1, . . . il≧n) by themethod given in Schurmann [AS1] (2000), Sect. 2.2. ‘Pattern (3.1) isapplied with probability p’ means: (3.1) is applied if los(1,a)=1 (iflos(1,a)=0 then (3.1) is not be applied), where a[1]=p and los isdefined as follows:

function los(k: integer, var ap: array[1 . . . 40] of real): integer;

var i: inleger; const am: array[1 . . . 40] of char=(‘a’. . . ‘z’,‘0’. .. ‘9’, ‘+’, ‘−’, ‘/’, ‘:’);

begin Ur:=box with 10000 not marked balls;

for i:=I to k do begin mark ap[i]*10000 not marked balls with the signam[i] end;

choose randomly a ball from Ur;

if the chosen ball is not marked then los:=0 else

begin i:=0; repeat i:=i+1 until (the chosen ball is marked with the signam[i]);

los:=i end end los.

If in (3.1) r=2 (the priority) and bef(vP,b,t+q ht) and des(vP,bt+q ht)are not determined or determined by a stimulus pattern with priority 2then pattern (3.1) is applied, with probability Nba/Nb, to calculationof values bef(vP,b,t+e*q ht) and des(vP,b,t+e*q ht) (e=1, . . . ,e1≧n)as in the case where r=1. If in (3.1) r=2 and bef(vP,b,t+q ht) anddes(vP,b,t+q ht) are determined by a pattern with priority 1 thenpattern (3.1) is not applied at time t.

Case: In situation SMn, for need b (and the pattern in SV.EvI), occursonly one pattern of the form (‘pr=’r, (Nba, Nb), vsb(vP,b)=(p;n;(y11,z11), . . . , (y1n,z1n); q ht) [^(∘)|/ z eu]; SV.Ev1 [^(∘)|;where C])

where s denotes the letter ‘n’ or ‘p’, SV is a situation or activitymodel, C is true and (‘pr=’r, (N1a, N1), esb(vP,b)=(n;(y1,z1), . . . ,(yn,zn); q ht) [^(∘)|/z eu] ^(∘)|; where C1]) occurs in SV.Ev1. Ifbef(vP,b,t+k*q ht) and des(vP,b,t+k*q ht) are determined by the patternin SV.Ev1, for k=1, . . . ,m, then execute the following operations,with probability Nba/Arb (i.e. if los(1,a)=1 and a[1]=Nba/Nb):

bef(vP,b,t+j*q ht)=bef(vP,b,t+j*q ht)+p*(y11−y1);

des(vP,b,t+j*q ht):=des(vP,b,t+j*q ht)+p*(z11−z1) for j=1, . . . ,m−n;

bef(vP,b,t+(m−n+i)*q ht):=bef(vP,b,t+(m−n+i)*q ht)+p*(y1i−yi);

des(vP,b,t+(m−n+i)*q ht):=des(vP,b,i+(m−n+i)*q ht)+p*(z1i−zi) for i−1, .. . ,n.

Case Following m patterns occur in SMn for need b

(3.2) (pr=′r, (Nbaj, Nb), efsj(vP,b)=(nj;(yj2, zj1), . . . ,(yjnj,zjnj); qj ht) [^(∘)|/zj euj] [^(∘)|; where Cj]), fur j=1, . . . ,m,where efsj denotes epb, epbu, enb, enbu, Nba1+. . . +Nbam≦Nb andcondition Cj holds at time t. If r=1 or r=2 and bef(vP,b,t+xt) unddes(vP,b,t+xt) (xt>0) are not determined by pattern with priority 1 thenexecute the following operations:

a[j]:=Nbaj/Nb, for=1, . . . ,m;

e:=los(m,a) {pattern e has been chosen if e>0};

if e=0 then ignore all patterns (3.2) (no pattern is applied);

if e>0 then calculate values bef(vP,b,t+k*qe hte) and des(vP,b,t+k*qehte) by the pattern efse(vP,b)=(ne;(ye1,ze1), . . . (in the same way asby pattern (3.1))

3.2. Determination of Values bef(vP,b,t) and des(vP,b,t) when anActivity is Executed

bef(vP,b,t) and des(vP,b,t) change when vP executes activity or anactivity uses vP (as an object). Model of an activity (or activityscheme, in short: activity) has the following form

AV begin {Ea1; . . . ;Eam}; (SG,KA) end

where Eai denotes a property (e g stimulus pattern) of the activity AVand (SG,KA) is a directed graph, without isolated nodes, where nodes aresituation models. To each edge (SMi, . . . ,SMj) (in KA) is associatedsub-activity

sbeh(SMi,SMj)=((Nja, Nj), fADij, Dsij)

where fADij is a sequence of elementary activities and operations whichlead, with probability Nja/Nj, from situation SMi to situation SMj (whenthis sequence is executed) and Dsij is a set of stimulus patterns.Activities (behaviour schemes) are described more exactly in Schurmann[AS3] (1998), Sect. 4.1.

When activity AV is executed (by vP) then bef(vP,b,t) and des(vP,b,t)are determined as follows When vP is in situation SMi and (SMi,SMje)(for e≦e1) are in KA then vP executes a sub-activitysbeh(SMi,SMjn)=((Njna, Njn), fADijn, Dsijn) (i e a sequence ofelementary activities belonging to fADijn) and achieves in this way nextsituation SMjn. bef(vP,b,t+xt) und des(vP,b,t+xt) are determined, atfirst, by stimulus patterns in Dsijn and after that by patternsoccurring in SMjn, as described in Sect. 3.1

4. Intensities bef(vP,b,t) und des(vP,b,t) in Standard Situations

In this section are described methods for determining bef(vP,b,t+xt) anddes(vP,b,t+xt) for needs AU, NE, GR, MA, LI, MB, NA, BZ(Sz) and BH(OK).

bef(vP,AU,t); AU—need for attention and identification. bef(vP,AU,t)changes in following cases (a1) caused by time and depending on state ofrelaxation of vP, (a2) when perceiving objects and situations, (a3) whenexecuting activities. In (a1) is determined the ground attention AU ofvP after a sleep. The greatest attention is perceived neither as painnor as grief

−4≦cau≦bef(vP,AU,t)≦oau≦25, cau<2, 5≦oau.

Attention (a2) and (a3) is directed to objects, situations andelementary activities/operations When des(vP,AU,t) increases (decreases)then the attention of vP increases (decreases, respectively). Thebehaviour ‘sleep’ increases

bef(vP,AU,t) up to oau—0.5. We distinguish the following kinds ofattention.

AUw(UOS)—attention when vP is identifying surrounding, object orsituation UOS,

AUa(AVe)—attention when vP is executing (elementary) activity/operationAVe.

The following rules AU1, . . . ,AU3 have priority 1.

AU1 Let ts denotes the time 5 min after good sleep of vP. The groundattention of vP is determined as follows

bef(vP,AU,ts)=oau −2, des(vP,AU,ts)=5.

Every r min (after time ts) are performed the following operations

dau1 bef(vP,AU,ts+(i−1)*r min)+gda*sqrt(i*2)*(oau+5−bef(vP,AU,ts+(i−1)*rmin));

dau2.=−ga1*sqrt(8+des(vP,ES,ts+1*r min); dau:=dau1+dau2;

bef(vP,AU,ts+i*r min):=max(min(dau, oau−1.5), cau);

des(vP,AU,ts+i*r min).=2.1*(oau−bef(vP,AU,ts+1*rmin); for i=1, . . .,i1≦865

(3 days=24 h*3=72*60 min, 72*60/5=864), where 1≦r≦20, ES denotes theneed for relaxation, 0.001≦gda≦0.1, 0.1≦ga1<1 (sugg.: r=5, gda=0.011,gal=0.4, oau=20, cau=−3)

AU2. vP has noticed (at time t) a new object or situation, OS, in partTUs of surrounding U(vP,t). The intensity of attention AUw directed toTUv equals:

bef(vP,AUw(TUs),t):=bef(vP,AU,t)−3;des(vP,AUw(TUs),t):=2.1*(oau−bef(vP,AUw(TUs),t)).

OS may be also a sequence of words which denotes an object, a situationor an activity

AU2.1. When vP identified (time t1) object or situation OS in TUs (thusvP has sufficient exact model of OS) then:

bef(vP,AUw(TUs),t1).=bef(vP,AU,t1); des(vP,AUw(TUs),t):=des(vP,AU,t1);

aw1.=gau*sqrt((des(vP,UA,t1)+0.2)*(0.333+max(pros(vP,OS,t1),nros(vP,OS,t1))));

if aw1≦oau−cau then bef(vP,AUw(OS),t1).=oau−2−aw1 elsebef(vP,AUw(OS),t1).=cau−2;

des(vP,AUw(OS),t1).=2.1*(oau−bef(vP,AUw(OS),t1));

where AUw(OS) is the attention directed to OS, 0.01≦gau≦0.1 (sugg.gau=0.0317). After vP finished observation of OS (time t2) inhis/her/its surrounding U(vP,t2) then:

bef(vP,AUw(OS),t2):=oau−0.5; des(vP,AUw(OS),t2):=1.

AU2.2 When vP is not able to identify OS (i e vP cannot associate withOS an appropriate model) then.

bef(vP,AUw(OS),t1)=bef(vP,AU,t1)−2.5;des(vP,AUw(OS),t1):=2.1*(oau−bef(vP,AUw(OS),t1)).

When vP has build model, OSM, of OS then determine valuesbef(vP,AUw(OS),t1) and des(vP,AUw(OS),t) as given in AU2 1. If vP hasnot build model of OS (time t3) then:

bef(vP,AUw(TUs),t3):=bef(vP,AU,t3);des(vP,AUw(TUs),t3):=1.5*(oau−bef(vP,AU,t3));

bef(vP,AUw(OS),t3):=max(bef(vP,AU,t3), 1);des(vP,AUw(OS),t3).=1.5*(oau−bef(vP,AU,t3)).

AU3 vP executes activity, AVa, different from a passive activity like‘sleep’ or ‘lie relaxed’ The attention of vP is directed to sub-activitysbeh(SMi,SMj)=(.fADij,Dsij) which vP is executing, where fADy is thesequence of elementary activities which vP is executing and Dsij is theset of stimulus patterns connected with activities in fADij. Thefollowing operations are performed before execution offADij (where gauis given in AU2.1):

aw1.=gau*sqrt((des(vP,UA,t)+0.2)*(0.333+max(pros(vP,Dsij,t),nros(vP,Dsij,t))));

if aw1≦oau−cau then bef(vP,AUa(fADij),t):=oau−2−aw1 elsebef(vP,AUa(fADij),t):=cau−2;

des(vP,AUa(fADij),t).=2.1*(oau−bef(vP,AUa(fADij),t));

aw2.=gau*sqrt((des(vP,UA,t)+0.2)*(0.333+max(pros(vP,SMj,t),nros(vP,SMj,t))));

if aw2≦oau−cau then bef(vP,AUw(SMj),t)=oau−2−aw2 elsebef(vP,AUw(SMj),t).=cau−2;

des(vP,AUw(SMj),t)=2.1*(oau−bef(vP,AUw(SMj),t)).

After vP has executed activities fADO (time t1) then

bef(vP,AUa(fADij),t1).=oau−1.5; des(vP,AUa(fADij),t1):=3.

After vP finished observation of SMj (time t2) then.

bef(vP,AUw(SMj),t2):=oau−0.5; des(vP,AUw(SMj),t2):=1.

bef(vP,NE,t); NVE—curiosity and need for knowledge. There are 3 kinds ofthe need NE:

NEw(OS)—when vP perceives object or situation OS,

NEk(OSM)—need for knowledge of object or situation model OSM and modelsassociated with OSM,

NEz(SM)—how can situation SM be achieved.

We assume that vP has a set (KAO) of cognition algorithms andoperations. Examples: for building visual representation of object orsituation, algorithms for perceiving properties of objects andsituations (e g touch properties of an object, motion properties),algorithms for naming objects, situations and activities (situation canbe named by a clause—as described in Schurmann [AS2] (1999)), algorithmsfor verifying and (eventual) correcting consistency and completeness ofobject and situation models, algorithms for reasoning about surroundingof vP (some such algorithms are given in Schurmann [AS3] (1998)).

Below, x denotes letters w, k or z in the contexts NEx, onx, cnx, rnx.Let 3≦one≦20 and −12≦cne>0, where one, cne depend on vP. It holds:

−0.6*nx≦cnx(arg)≦bef(vP,NEx(arg),t)<onx(arg)≦rnx.

NE1. Every 55 days are executed (with priority 3) the followingoperations:

onx(arg).=max(onx(arg)−dn1, 0.6); cnx(arg):=min(cnx(arg)+0.6*dn1, 0);

bef(vP,NEx(arg),t).=min(bef(vP,NEx(arg),t)+dnb, onx(arg));

des(vP,NEx(arg),t).=max(des(vP,NEx(arg),t)−2.2*dnb, 0.2);

where 0.01≦dn1≦1 and 0.05≦dnb≦4 (sugg.dn1=0.2, dnb=0.25).

The following rules NEw1, . . . ,NEz2.3 have priority 2. Assume, vP hasnoticed (at time t) new object or situation, OS, in his/her/itssurrounding U(t).

NEw1 When vP has identified OS as model OSM (i e the result ofalgorithms for identification is ‘OSM is good model of OS’) and has notidentified any new property of OS then do not change bef(vP,NEw(OSM),t)and des(vP,NEw(OSM),t).

NEwk1.1. When vP has identified OS as model OSM and the result ofidentification algorithms is ‘OS has new property En’ then.

dnw:=onw(OSM)−cnw(OSM);Nw1=gn1*sqrt(dnw)*1n(1.1+0.2*(bef(vP,NEw(OSM),t)−cnw(OSM)));

bef(vP,NEw(OSM),t)=max(bef(vP,NEw(OSM),t)−Nw1, cnw(OSM));

des(vP,NEw(OSM),t)=min(des(vP,NEw(OSM),t)+1.8*Nw1, 2*dnw);

where 0.01≦gn1≦1 (sugg.: gn1=0.34)

NEwk1.2. When the property En (mentioned in NEwk1.1) has been entered inthe model OSM by appropriate perceiving algorithm (time t1) then.

dnk.=onk(OSM)−cnk(OSM);Nw1.=gn2*sqrt(dnk)*In(1.1+0.2*(bef(vP,NEk(OSM),t1)−cnk(OSM)));

bef(vP,NEw(OSM),t1)=max(bef(vP,NEw(OSM),t)−Nw1, cnw(OSM));

des(vP,NEk(OSM),t1):=min(des(vP,NEw(OSM),t1)+1.8*Nk1, 2*dnw);

where 0.01≦gn2≦1 (sugg. gn2=0.34).

NEwk1.3. If, after the new property En (mentioned in NEwk1.1) has beenentered into OSM, the result of applied consistence algorithms (time t2)is ‘no essential inconsistency of the model OSM is found’ then:

onw(OSM):=min(onw(OSM)+an1, rnw); cnw(OSM).=max(cnw(OSM)−0.6*an1,−0.6*rnw);

dnw.=onw(OSM)−cnw(OSM);Nw2.=gnw*sqrt(dnw)*ln(1.1+0.2*(onw(OSM)−bef(vP,NEw(OSM),t2)));

bef(vP,NEw(OSM),t2):=min(bef(vP,NEw(OSM),t2)+Nw2, onw(OSM));

des(vP,NEw(OSM),t2).=max(des(vP,NFw(OSM),t2)−1.6*Nw2, 1);

onk(OSM):=min(onk(OSM)+an1, rnk); cnk(OSM):=max(cnk(OSM)−0.6*an1,−0.6*rnk);

dnk.=onk(OSM)−cnk(OSM);Nk2.=gnw*sqrt(dnk)*1n(1.1+0.2*(onk(OSM)−bef(vP,NEk(OSM),t2)));

bef(vP,NEk(OSM),t2).=min(bef(vP, NEk(OSM),t2)+Nk2, onk(OSM));

des(vP,NEk(OSM),t2):=max(des(vP,NEk(OSM),t2)−1.6*Nk2, 1);

where 0.01≦gntw≦1, 0.05≦an1≦1 (sugg.: gnw=0.33, an1=0.25).

NEwk1.4. If, after the new property En (mentioned in NEwk1.1) has beenentered into OSM, the result of applied consistence algorithms (time t2)is ‘the model OSM is not consistence with/because.’, then

onw(OSM)=max(onw(OSM)−0.7*an1, 0.6); cnw(OSM).=min(cnw(OSM)+0.5*an1, 0);

dnw:=onw(OSM)−cnw(OSM);Nw2:=gnw*sqr1(dnw)*1n(1.1+0.2*(onw(OSM)−bef(vP,NEw(OSM),t2)));

bef(vP,NEw(OSM),t2).=min(max(bef(vP,NEw(OSM),t2)+0.2*Nw2, cnw(OSM)),onw(OSM));

des(vP,NEw(OSM),t2).=min(max(des(vP,NEw(OSM),t2)−0.8*Nw2, 0.2),1.6*dnw);

onk(OSM).=max(onk(OSM)−0.7*an1, 0.5); cnk(OSM).=min(cnk(OSM)+0.5*an1,0);

dnk.=onk(OSM)−cnk(OSM);Nk2=gnw*sqrt(dnk)*1n(1.1+0.2*(onk(OSM)−bef(vP,NEk(OSM),t2)));

bef(vP,NEk(OSM),t2).=min(max(bef(vP,NEk(OSM),t2)+0.2*Nk2,cnk(OSM)),onk(OSM));

des(vP,NEk(OSM),t2)=min(max(des(vP,NEk(OSM),t2)−0.8*Nk2, 0.2), 1.6*dnk);

where 0.01≦gnw≦1, 0.05≦an1≦1 (sugg.: gnw=0.33, an1=0.25)

NEwk2. If identification of OS has resulted in ‘vP has no good model ofOS; the best model of OS is OSMu’ (time t) then:

build model, OSMs, of OS;

onw(OSM):=min(onw(OSMu)+an1, rnw);

cnw(OSMs)=max(cnw(OSMu)−0.6*an1, −0.4*rnw); dnw.=onw(OSMs)−cnw(OSMs);

bef(vP,NEw(OSMs),t):=onw(OSMs)−0.3*dnw; des(vP,NEw(OSMs),t)=0.5*dnw;

onk(OSMs):=min(onk(OSMu)+an1, rnk); cnk(OSMs):=max(cnk(OSMu)−0.6*an1,−0.4*rnk);

dnk.=onk(OSMs)−cnk(OSMs); bef(vP,NEk(OSMs),t):=onk(OSMs)−0.3*dnk;

des(vP,NEk(OSMs),t):=0.5*dnk.

Property En in NEwk1.1, . . . ,NEwk1.4 and OS in NEwk2 may be asequence, fws, of words, e g room, person rides a horse. If OS in NEwk2is a word sequence fws then model, representing the meaning of fws, isbuilt, where fws is the name of this model (semantic of such clauses isoutlined in Schurmann [AS2], (1999)).

NEwk3.1 If the result of algorithms for consistency and completenesswhich were applied to model OSM and models related to OSM (time t) is‘inconsistency and incompleteness of the model OSM is not found’ then.

onw(OSM)=min(onw(OSM)+an1, rnw); cnw(OSM):=max(cnw(OSM)−0.6*an1,−0.6*rnw);

bef(vP,NEw(OSM),t):=min(bef(vP,NEw(OSM),t)+1.6*Nw2, onw(OSM));

des(vP,NEw(OSM),t):=max(des(vP,NEw(OSM),t)−2.6*Nw2, 1);

onk(OSM):=min(onk(OSM)+an1, rnk); cnk(OSM)=max(cnk(OSM)−0.6*an1,−0.6*rnk);

bef(vP,NEk(OSM),t)=min(bef(vP,NEk(OSM),t)+1.6*Nk2, onk(OSM));

des(vP,NEk(OSM),t) max(des(vP,NEk(OSM),t)−2.6*Nk2, 1);

where Nw2 and Nk2 are given in NEwk1.3.

NEwk3.2. If the result of algorithms for consistency and completenesswhich were applied to model OSM and models related to OSM (at time t)is: ‘model OSM is not consistence with/because . . . ’ or ‘model OSM isnot complete’, then:

onw(OSM).=max(onw(OSM)−1.4*an1, 0.6); cnw(OSM):=min(cnw(OSM)+an1, 0);

dnw, Nv2 and bef(vP,NEw(OSM),t2)−as in NEwk1 4;

des(vP,NEw(OSM),t2):=min(max(des(vP,NEw(OSM),t2)−1.4*Nw2, 0.2),1.6*dnw);

onk(OSM):=max(onk(OSM)−1.4*an1, 0.5); cnk(OSM):=min(cnk(OSM)+an1, 0);

dnk, Nk2—as in NEwk1.4,

bef(vP,NEk(OSM),t2):=min(max(bef(vP,NEk(OSM),t2)+0.1*Nk2, cnk(OSM)),onk(OSM));

des(vP,NEk(OSM),t2):=min(max(des(vP,NEk(OSM),t2)−1.5*Nk2, 0.2),1.5*dnk);

NEw2.1. When (i) vP executed cognition activity AVE1 (in time (t1,t2))in order to know whether model OSM have property Em, (ii) the executionof AVE1 decreased bef(vP,be,t1) by ds(be) or prevented the increase ofbef(vP,be,t1) by ds(be), for e=1, . . . u, (iii) vP did not find outwhether OSM has or has not the property Em, then:

(4.1.1) KA(Em,AVEi)=des(vP,b1,t2)*ds(b1)+. . . +des(vP,bu,t2)*ds(bu);

(4.1.2) if gKA (Em) is not entered in OSM then gKA(Em):=KA(Em,AVEt) elsegKA(Em):=gKA(Em)+KA (Em,AVEt); enter gKA(Em) into OSM;

Nw3:=gnw1*1n(1.1+0.2*(bef(vP,NEw(OSM),t2)−cnw(OSM)))*(0.2+sqrt(gKA(Em)));

bef(vP,NEw(OSM),t2):=max(bef(vP,NEw(OSM),t2)−Nw3, cnw(OSM));

des(vP,NEw(OSM),t2).=min(des(vP,NEw(OSM),t2)+1.4*Nw3,2*(onw(OSM)−cnw(OSM));

where 0.04≦gin1≦0.7 (sugg.gnw1=0.15).

KA(Em,AVEi) can be equal to 0. Example of a cognition activity. vP getinformation about OSM from a person, from vPa or a book

NEw2.2. If conditions (i) and (ii) in NEw2.1 hold and the result of theactivity AVEi is either ‘OSM has the property Em’ or ‘OSM has not theproperty Em’ then,

execute operations (4.1.1) and (4.1.2),Nwo:=gwo*1n(1+rnw−onw(OSM))*(0.2+sqrt(gKA(Em)));

onw(OSM).=min(onw(OSM)+Nwo, rnw); cnw(OSM).=max(cnw(OSM)−0.7*Nwo,−0.6*rnw);

Nw4.=gnw2*1n(1.1+0.2*(onw(OSM)−bef(vP,NEw(OSM),t2)))*(0.2+sqrt(gKA(Em)));

bef(vP,NEw(OSM),t2).=min(bef(vP,NEw(OSM),t2)+Nw4, onw(OSM));

des(vP,NEw(OSM),t2).=max(des(vP,NEw(OSM),t2)−1.7*Nw4, 1);

where 0.005≦gwo≦0.1, 0.02≦gnw2≦0.8 (sugg: gwo=0.035, gnw2=0.19)

NEw2.3. When vP concludes (time t3≧t2) that she/he/it cannot executemore cognition activities AVEt in order to know whether OSM should havethe property Em then:

Nwo1.=gwo1*1n(1+onw(OSM)−cnw(OSM))*(0.2+sqrt(gKA(Em)));

onw(OSM):=max(onw(OSM)−Nwo1, 0.6); cnw(OSM):=min(cnw(OSM)+0.6*Nwo1, 0);

bef(vP,NEw(OSM),t3):=min(max(bef(vP,NEw(OSM),t3), cnw(OSM)), onw(OSM));

des(vP,NEw(OSM),t3):=min(des(vP,NEw(OSM),t3)−gnw3*1n(1.1+des(vP,NEw(OSM),t3))*(0.2+sqrt(gKA(Em))),1)

where 0.005≦gwo1≦0.1, 0.02≦gnw3≦0.9 (sugg:gwo1=0.035, gnw3=0.24).

NEwk4. When (i) cognition algorithms of vP found out that object modelsM(O1), . . . ,M(On) have properties E1, . . . ,Eu (u>1, n>3), (ii) vPhas built generalized object model M(Og) having the properties E1, . . .,Eu, then

onw(M(Og))=max(onw(M(Oi)), for 0<i≦n); cnw(M(Og)):=−0.6*onw(M(Og));

dnw.=onw(M(Og))−cnw(M(Og));

bef(vP,NEw(M(Og)),t).=onw(M(Og))−0.15*dnw;des(vP,NEw(M(Og)),t):=0.3*dnw);

onk(M(Og)).=max(onk(M(Oi)), for 0<i−n); cnk(M(Og)):=−30.6*onk(M(Og));

dnk.=onk(M(Og))−cnk(M(Og));

bef(vP,NEk(M(Og)),t).=onk(M(Og))−0.2*dnk;des(vP,NEk(M(Og)),t).=0.36*dnk).

bef(vP,NEz(SM),t). Let ES(t) be the set of situations (i e situationmodels) which vP can achieve in the present time, and SMz situationwhich does not belong to ES(t) and which vP wants to achieve.

NEz1. If onz(SMz), bef(vP,NEz(SMz),t) are not defined then:

onz(SMz):=min(onw(SMz), 0.6*rnz); cnz(SMz).=−0.6*onz(SMz);Bd(vP).=Bd(vP)∪{NEz(SMz)};

bef(vP,NEz(SMz),t):=onz(SMz)−0.2*(onz(SMz)−cnz(SMz));des(vP,NEz(SMz),t)=0.36*(onz(SMz)−cnz(SMz)).

NEz2.1. When (i) vP executed activity (e g cognition algorithms) AKA (intime (t1,t2)) in order to build new activity AVSz such that theexecution of AVSz leads from at least one situation in ES(t1) to thesituation SMz, (ii) the execution of AKA decreased bef(vP,be,t1) byds(be)>0 or prevented the increase of bef(vP,be,t1) by ds(be), for e=1.. . , (iii) vP could not build activity AVSz by activity AKA, then

(4.2 1) AK(AVSz,AKA).=des(vP,b1,t2)*ds(b1)+. . . +des(vP,bu,t2)*ds(bu);

(4.2.2) if gAK(AVSz) is not entered in SMz then gAK(AVSz):=AK(AVSz,AKA)

else gAK(AVSz).=gAK(AVSz)+AK(AVSz,AKA); enter gAK(AVSz) into SMz;

Nz1:=gnz*1n(1.1+0.2*(bef(vP,NEz(SMz),t2)−cnz(SMz)))*(0.2+sqrt(gAK(AVSz)));

bef(vP,NEz(SMz),t2).=max(bef(vP,NEz(SMz),t2)−Nz1, cnz(SMz));

des(vP,NEz(SMz),t2).=min(des(vP,NFz(SMz),t2)+1.6*Nz1,2*(onz(SMz)−cnz(SMz));

where 0.05≦gnz≦0.7 (sugg gnz=0.17).

NEz2.2. When conditions (i), (ii) in NEz2.1 hold and vP has builtactivity AVSz by AKA then: execute operations (4 2 1) and (4 2 2);

Nzo:=gzo*1n(1+rnz−onz(SMz))*(0.2+sqrt(gAK(AVSz)));

onz(SMz).=min(onz(SMz)+Nzo, rnz); cnz(SMz).=max(cnz(SMz)−0.7*Nzo,−0.6*rnz);

Nz2:=gnz2*1n(1.1+0.2*(onz(SMz)−bef(vP,NEz(SMz),t2)))*(0.2+sqrt(gAK(AVSz)));

bef(vP,NEz(SMz),t2):=min(bef(vP,NEz(SMz),t2)+Nz2, onz(SMz));

des(vP,NEz(SMz),t2):=max(des(vP,NEz(SMz),t2)−1.8*Nz2, 1);

where 0.005≦gzo≦0.15, 0.02≦gnz2≦0.7 (sugg.: gzo=0.035, gnz2=0.19).

Ez2.3. When vP concludes (time t3≧t2) that he/she/it cannot execute moreactivities of the kind AKA in order to build activity AVSz then.

Nzo1:=gzo1*1n(1+onz(SMz)−cnz(Smz))*(0.1+sqrt(gAK(AVSz)));

onz(SMz):=max(onz(SMz)−Nzo1, 0.6); cnz(SMz).=min(cnz(SMz)+0.6*Nzo1, 0);

bef(vP,NEz(SMz),t3):=max(min(bef(vP,NEz(SMz),t3), onz(SMz)), cnz(SMz));

des(vP,NEz(SMz),t3).=max(des(vP,NEz(SMz),t3)−gn3*1n(1.1+des(vP,NEz(SMz),t3))*(0.2+sqrt(gAK(AVSz))),1);

where 0.005≦gzo1≦0.1, 0.02≦gn3≦0.9 (sugg.: gzo1=0.035, gn3=0.24).

Situation SMa in ES(t) and SMz may have the following meanings: SMa−‘vPhas objects O1, . . . , Ok’, SMz−‘object Ogb is built from objects O1, .. . ,Ok’. AVSz is then the activity (the method) which builds the objectOgb from objects O1, . . . ,Ok.

bef(vP,GR,t); GR—to belong to communities. Below, instead of GR we useGR(G)—the need to belong to community G. Each community G has a setNRV(G) of norms, principles, rules and behaviour schemes (models) whichought to be respected and obeyed by members of the community. GR(G) is asecondary need. It emerges in vP when the following conditions aresatisfied.

gr1 Σ_(PεT) rosa(vP,P,t)>20* |T|, where T is the set of these members Pof the community G for whom vP has model M(P) and |T| denotes the numberof elements of set T;

gr2. the result of consistency algorithms applied to NRV(G) is ‘NRV(G)does not contradict the norms, rules and behaviours in NRV(vP) andNRV(Ga) for communities Ga to which vP belongs’, where NRV(vP) denotesthe norms, rules and behaviours of vP;

gr3. vP executed activities AVvk(Pk1, . . . ,Pknk) (k=1, . . . ,r)together with members Pk1, . . . , Pknk of the community G, in time(t1k, t2k) (where t2k≦t), and vP has perceived that AVvk(Pk1, . . .,Pknk) altered bef(vP,bki,t1k) by

dy(bki,t2k), for i=1, . . . , tk, and r=0 or Zb(G)>2 if r>0, whereZb(G)=0 if r=0, and

Zb(G)=Σ_(k=1 r)Σ_(n=1 tk)sqrt(desm(bki,t2k)*|dy(bki,t2k|)*sign(dy(bki,t2k)), if r>0

desm(bkt, t2k)=max(des(vP,bki, t1k), des(vP,bki,t2k));

gr4. members Pje (e=1, . . . uj) of the community G executed activitiesAVu(u=1, . . . ,w), in times (t1u, t2u) (where t2u≦t), and vP hasperceived that AVu altered bef(vP,bui,t1u) by dy(bui,t2u), for i=1, . .. ,nu, and w=0 or ZPb(G)>2 if w>0, where ZPb(G)=0 if w=0, and

ZPb(G)=Σ_(n=1 w)Σ_(n=nw)sqrt(dem(bui,t2u)*|dy(bui,t2u)|)*sign(dy(bui,t2u)), if w>0;

gr5 vP perceives—believes—(at time t) that if she/he/it belongs to thecommunity G then vP will be able to execute activities AVh1, . . . ,AVhmsuch that AVhi would increase bef(vP,bhie,t) by sdy(bhie,t), for e=1, .. . , and m=0 or

Zer(G)>3 if m>0, where Zer(G)=0 if m=0, and

Zer(G)=Σ_(i=1 m) Σ_(e=1) sqrt(des(vP,bh1e,t)*sdy(bhie,t)), if m>0.

GR1. When conditions gr1, . . . ,gr5 are satisfied andZb(G)+ZPb(G)+Zer(G)>5 (time t) then

Bd(vP):=Bd(vP)∪{GR(G)}; NRV(vP):=NRV(vP)∪NRV(G);

ogr(G)=min(max(gc1*(Zb(G)+ZPb(G)+Zer(G)), 1), 16); cgr(G):=−0.6*ogr(G);

bef(vP,GR(G),t):=ogr(G)−0.1*(ogr(G)−cgr(G)); des(vP, GR(G),t).=1;

where 0.05≦gc1≦0.4 (sugg. gc1=0.1).

GR2. If GR(G)εBd(vP), vP executed activity AVv(P1, . . . ,Pn) togetherwith members P1, . . . ,Pn of the community G, in time (t1, t2), and vPhas perceived that AVv(P1, . . . ,Pn) altered bef(vP,bi,t1) bydy(bi,t2), for i=1, . . . ,u, then:

Zub=Σ _(t=) sqrt(max(des(vP,bi,t1),des(vP,bi,t2))*|dy(bi,t2)|)*sign(dy(b1,t2));

ogr(G):=min(ogr(G)+gc2*Zub, 28); cgr(G)=max(cgr(G)−0.6*gc2*Zub, −25);

bef(vP,GR(G),t2).=min(max(bef(vP,GR(G),t2)+gb1*Zub, cgr(G)), ogr(G));

des(vP,GR(G),t2):=max(min(des(vP,GR(G),t2)−1.7*gb1*Zub,2*(ogr(G)−cgr(G)), 0.5);

where 0.03≦gc2≦0.4, 0.03≦gb1≦0.8 (sugg.gc2=0.1, gb1=0.25).

GR3. Time dependent alteration of bef(vP,GR(G),t). Every 30 days executefollowing operations, with priority 3:

ogr(G)=max(ogr(G)−gro*1n(1.1+ogr(G)), 0.5);cgr(G)=min(cgr(G)+gro*1n(1.1+|cgr(G|), 0);

if bef(vP,GR(G),t)≧0 thenbef(vP,GR(G),t):=max(bef(vP,GR(G),t)−grz*1nn(1.1+bef(vP,GR(G),t)

−egr(G)), 0) else bef(vP,GR(G),t):=min(max(bef(vP,GR

(G),t), cgr(G)), ogr(G));

des(vP,GR(G),t)=min(des(vP,GR(G),t)+grd*1n(20+des(vP,GR(G),t)),1.8*(ogr(G)−cgr(G));

where 0.002≦gro≦0.2, 0.002≦grz≦0.2, 0.002≦grd≦0.2 (sugg.gro=0.027,grz=0.03, grd=0.031)

Rules GR1, GR2, GR4, GR5 have priority 2. bef(vP,GR(G),t) decreases whenvP is in situation Sg1 and Sg2:

Sg1: The result of perceiving and cognition algorithms of vP (at time t)is: if I executed activity AVw together with members of the community Gthen bef(vP,bwi,t) would alter by day(bwi), for i=1, . . . ,u>0, sothat:

Zua(t)=Σ_(t=1 w) sqrt(des(vP,bwi,t)*|day(bwi,t|)*sign(day(bwi))>2.

Sg2: Because members of the community G cannot execute or do not want toexecute activity AVw together with vP (e g vP is separated from thecommunity G), vP executes activity AVr (instead AVw), in time (t,t1),which alters bef(vP,bwi,t) by dy(bwi,t1), for i=1, . . . u, so that:

dZug(t1)=Zua(t)−Σ_(t=1) sqrt(max(des(vP,bwi,t),des(vP,bwi,t1))*|dy(bwi,t1|)* sign(dy(bwi,t1))>2.

GR4. When vP is in situation Sg1 and Sg2 then:

ogr(G):=min(ogr(G)+gr1*dZug(t1), 26);cgr(G).=max(cgr(G)−0.7*gr1*dZug(t1), −25);

bef(vP,GR(G),t1).=max(bef(vP,GR(G),t1)−2.5*gr1*dZug(t1), cgr(G));

des(vP,GR(G),t1):=min(des(vP,GR(G),t)+4.5*gr1*dZug(t1),1.8*(ogr(G)−cgr(G));

where 0.03≦gr1≦0.4 (sugg. gr1=0.1).

GR5. When members of community G executed activity AVgv, in time (t,t1), and vP has perceived that AVgv hanged bef(vP,bgi,t) by dy(bgi), fori=1, . . . ,u, so that

Zug(t1)=Σ_(n=1 w) sqrt(max(des(vP,bgi, t),des(vP,bgi,t1))*|dy(bgi)|)*sign(dy(bgi))<−2

then:

ogr(G):=max(ogr(G)+gr3*Zug(t1), 0.5);cgr(G):=min(cgr(G)−0.7*gr3*Zug(t1), 0);

bef(vP,GR(G),t1).=min(max(bef(vP,GR(G),t1)+3*gr3*Zug(t), cgr(G)),ogr(G));

des(vP,GR(G),t1).=max(des(vP,GR(G),t1)+gr3*Zug(t1), 0);

where 0.03≦gr3≦0.4 (sugg. gr3=0.1).

bef(vP,MA,t); MA—to have power over people and animals. Let OP and OPj(j=1,2, . . . ) denote an artificial creature (e g vP), a virtualorganization or institution, virtual human or animal, virtual group ofpeople, virtual deity Human has an innate need for power over people andanimals. We assume,

−28≦cma(OP)≦bef(OP,MA,t)≦oma(OP)≦30 and bef(OP,MA,ts)=0.4*oma(OP) is theinitial value, where oma(OP)>0, oma(OP) and cma(OP) are determinedindividually for each OP. Below, instead of cma(OP) and oma(OP), wewrite cma and oma.

bef(vP,MA,t) and des(vP,MA,t) change in following cases.

m1. vP ordered OP to execute or to stop activity AVop, or vP executedactivity AVv to cause OP to execute or to stop activity AVop (OP stopsactivity AVop if OP breaks off this activity or OP will not do thisactivity in future);

m2. vP has made OP harm and OP has to bear it (vP harms OP when vPdecreases bef(OP,b,.), for some needs bεBd(OP));

m3 vP gets more power or vP loses power;

m4. time dependent decrease of desire for power.

In order to formulate situations m1, m2, m3 more precisely, we introducenew stimulus pattern and situations Sub, Sun, SVub, SVun which describethe power relations between vP and OP. The new stimulus pattern has theform:

((Nba, Nb), eru(OP,b)=x; where C)

where −50≦x≦50 and C denotes a condition. If condition C holds and thispattern is applied then bef(OP,b,t), des(OP,b,t), epr(OP,eru, . . . )and enr(OP,eru, . . . ) are determined as follows

(4.3) if x≧0 then begin

Zu.=gd*x*1n(1+0.1*(og(b)−bef(OP,b,t))); bef(OP,b,t).=min(bef(OP,b,t)+Zu,og(b));

des(OP,b,t).=max(des(OP,b,t)−1.7*Zu, 1) end

else been Zu.=gd*x*1n(1+0.1*(bef(OP,b,t)−cg(b)));bef(OP,b,t).=max(bef(OP,b,t)+Zu, cg(b));

des(OP,b,t)=min(des(OP,b,t)−1.7*Zu, 2*(og(b)−cg(b))) end;

where

cg(b)≦bef(OP,b,t)≦og(b), 0.6≦gd≦1.2 (sugg. gd=0.9).

epr(OP,eru,b,a,t):=(Nba/Nb)*des(OP,b,t)*dzu*23, if x≧0;

enr(OP,eru,b,a,t):=(Nba/Nb)*des(OP,b,t)*dab*23, if x≦0;

where dzu=bef(OP,b,t)−bfb, dab=bfb−befOP,b,t,), bfb is the value ofbef(OP,b,t) before execution of operation

(4.3) and bef(OP,b,t) is the value after execution of operation (4.3).

Sub: Mub(vP,OP,Npa,Np;(Ber;Cu); dpm(vP); nRM(OP))

where: Npa≦Np, Npa and Np are natural numbers, Ber—(the scope of powerof vP over OP) is a set of orders which vP may give OP, Cu—conditionsfor execution of orders from Ber, dpm(vP)—some patterns of the form‘(Nma, Nm), eru(vP,MA)=x; where C’, where x≧0, nRM(OP)—a set of negativestimulus patterns of the form ‘(Nobia, Nobi), fsn(OP,bi)=. . . ; whereCAi’, where fsn denotes enb, unb, enbu, unbu, vpb, eru The meaning: WhenvP gives an order belonging to Ber to OP and conditions in Cu hold thenOP will execute this order with probability Npa/Np. If OP does notexecute this order (although conditions in Cu hold) then OP will bepunished (with probability Nobia/Nobi) by negative stimulus according toappropriate pattern ‘ . . . fsn(OP,bi)=.’ in nRM(OP). When vP achievessituation Sub then bef(vP,MA,.) increases according to appropriatepattern in dpm(vP).

Sun: Mun(vP, OP, (Berop;Ct); dnm(vP); Nua,Nu, nRM(vP)),

where. Berop—(the scope of power of OP over vP) a set of orders which OPmay give to vP, Cu—conditions for execution of orders in Berop,rosa(vP,Anu,t)<0 for Anu in Berop, dnm(vP)—some patterns of the form‘(Nma, Nm), eru(vP,MA)=y; where C’, where 0≧y, nRM(vP)—a set of negativestimulus patterns of the form ‘(Nobia, Nob1), fsn(vPbi)= . . . ; whereCAi’. The meaning. When vP has received order AnuεBerop from OP andconditions in Cu hold then vP must execute the order Anu. If vP does notexecute order Anu (although conditions in Cu hold) then vP will bepunished (with probability Nua/Nu) by negative stimulus according toappropriate pattern ‘ . . . fsni(vP,bi)= . . . ’ in nRM(vP).

SVub: MVub(vP, AVv, OP, Nvoa,Nvo; AVop; where Cu). The meaning. When vPhas executed activity AVv and conditions in Cu hold then vP expects thatOP executes (or stops) activity AVop with probability Nvoa/Nvo.

SVun. MVun(OP, AVo; vP, AVp; where Cu; Nsa,Ns, SAVs),

where SAVs denotes a situation or an activity, |rosa(vP,SAVs,t)|>2 androsa(vP,AVp,t)<−2. When OP executes activity AVo, conditions in Cu holdand vP does not execute (or stop) activity AVp then vP will be (withprobability Nva/Ns) in one of the following two situations:

(un1) vP is in situation SAVs (or must execute activity SAVs,respectively), if rosa(vP,SAVs,t)<−2;

(un2) vP does not achieve situation SAVs or vP must leave situation SAVs(or vP cannot execute activity SAVs), if rosa(vP, SAVs, t)>2.

If vP executes activity AVp then vP will be neither in situation (un1)nor in situation (un2).

The following rules (they have priority 2, except MA4) describe moreexactly changes of bef(vP,MA,t).

MA1.1. When (i) vP is in situation Sub, (ii) vP has given order An1εBerto OP (time t), where conditions in Cu hold, (iii) OP executed the orderAn1, then.

Maz1.=gmz1*(d1−Npa/Np)*1n(1.1+0.2*(oma−bef(vP,MA,t)))*sqrt(1+eres(vP,An1,t));

bef(vP,MA,t).=min(bef(vP,MA,t)+Maz1, oma);des(vP,MA,t):=max(des(vP,MA,t)−1.2*Maz1, 1);

where

1≦d1≦1.4, 0.005≦gmz1≦0.3 (sugg.: d1=1.2, gmz1=0.08) anderes(vP,An1,t)=rosa(vP,An1,t) if rosa(vP,An1,t)>0 (and is defined),otherwise eres(vP,An1,t)=0.

MA1.1.1 If conditions (i) and (ii) in MA1.1 hold and OP refused toexecute order An1 then.

Mab1.=gma1*(a1+Npa/Np)*1n(1.1+0.2*(bef(vP,MA,t)−cma))*sqrt(1+eres(vP,An1,t));

bef(vP,MA,t).=max(bef(vP,MA,t)−Mab1, cma);des(vP,MA,t):=min(des(vP,MA,t)+1.8*Mab1, 1);

where 0≦a1≦0.4, 0.008≦gma1≦0.5 (sugg. a1=0.05, gma1=0.12).

MA1.2. When (i) vP is in Situation Sun, (ii) vP has received orderAnuεBerop from OP, where conditions in Cu hold, (iii) vP has executedorder Anu (time t), then:

M12.=gm12*(d1−Nua/Nu)*1n(1+0.2*(bef(vP,MA,t)−cma))*sqrt(1+|rosa(vP,Anu,t)|);

bef(vP,MA,t).=max(bef(vP,MA,t)−M12, cma);des(vP,MA,t):=min(des(vP,MA,t)+1.6*M12, 2*(oma−cma)));

where 0.005≦gm12≦0.4 (sugg.: gm12=0.08) and d1 is given in MA1.1

MA1.2.1 When conditions (i) and (ii) in MA1.2 hold, vP has refused toexecute the order Anu and vP has not been punished by negative stimuligiven in nRM(vP,Anu), then

Ma3:=gm11*(a1+Nua/Nu)*1n(1.1+0.2*(oma−bef(vP,MA,t)))*sqrt(1+|rosa(vP,nRM(vP,Anu),t)|);

bef(vP,MA,t):=min(bef(vP,MA,t)+Ma3, oma);des(vP,MA,t).=max(des(vP,MA,t)−1.3*Ma3, 1);

where 0.006≦gm11≦0.6 (sugg gm11=0.09), a1 is given in MA1.1.1 andnRM(vP,Anu) are the patterns in nRM(vP) which are applied when vPrefuses to execute the order Anu.

MA1.2.2 When conditions (i) and (ii) in MA1.2 hold, vP has refused toexecute the order Anu and vP has been punished by negative stimuliaccording to patterns in nRM(vP,Anu), then:

M22=gm2*(d1−Nua/Nu)*1n(1.1+0.2*(bef(vP,MA,t)−cma))*sqrt(1+|rosa(vP,nRM(vP,Anu),t)|);

bef(vP,MA,t):=max(bef(vP,MA,t)−M22, cma);des(vP,MA,t)=min(des(vP,MA,t)+2*M22, 2*(oma−cma)));

where 0.008≦gm2≦0.7 (sugg. gm2=0.165) and d1 is given in MA1.1.

MA1.3. When (i) vP is in situation SVub, (ii) vP has executed activityAVv with respect to OP, where conditions in Cu hold, (iii) OP executed(or stopped execution of) activity AVop (time t) as vP has wished, then.

Maz2.=gmz2*(d1−Nvoa/Nvo)*1n(1.1+0.2*(oma−bef(vP,MA,t)))*sqrt(1+eres(vP,AVop,t));

bef(vP,MA,t).=min(bef(vP,MA,t)+Maz2, oma);des(vP,MA,t):=max(des(vP,MA,t)−1.2*Maz2, 1);

where 0.006≦gmz2≦0.6 (sugg. gmz2=0.09) and d1, eres are given in MA1.1.

MA1.3.1. When conditions (i) and (ii) in MA1.3 hold and OP refused toexecute (or to stop) activity AVop (time t) then:

Mab2=gma2*(a1+Nvoa/Nvo)*1n(1.1+0.2*(bef(vP,MA,t)−cma))*sqrt(1+eres(vP,AVop,t));

bef(vP,MA,t):=max(bef(vP,MA,t)−Mab2, cma);des(vP,MA,t):=min(des(vP,MA,t)+1.8*Mab2, 2*(oma−cma));

where 0.006≦gma2≦0.6 (sugg. gmz2=0.13) and a1 is given in MA1.1.1.

MA1.4. When (i) vP is in situation SVun, (ii) OP has executed activityAVo with respect to vP, where conditions in Cu hold, (iii) vP hasexecuted (or stopped the execution of) activity AVp (time t) as OPwished, then:

Mav1.=gmv*(d1−Nsa/Ns)*1n(1.1+0.2*(bef(vP,MA,t)−cma))*sqrt(1+|rosa(vP,AVp,t)|);

bef(vP,MA,t):=max(bef(vP,MA,t)−Mav1, cma);des(vP,MA,t):=min(des(vP,MA,t)+1.7*Mav1, 2*(oma−cma));

where 0.006≦gmv≦0.6 (sugg. gmv=0.09) and d1 is given in MA1.1.

MA1.4.1. When conditions (i) and (ii) in MA1.4 hold, vP has not executed(does not stop execution of) activity AVp and neither (un1) nor (un2)(in SVun) has taken place (Zeitp t), then:

Mavz:=gmov1*(a1+Nsa/Ns)*1n(1.1+0.2*(oma−bef(vP,MA,t)))*sqrt(1+|rosa(vP,AVp,t)|);

bef(vP,MA,t).=min(bef(vP,MA,t)+Mavz, oma);des(vP,MA,t):=max(des(vP,MA,t)−1.3*Mavz, 1);

where 0.006≦gmov1≦0.6 (sugg gmov1=0.07) and a1 is given in MA1.1.1.

MA1.4.2. When conditions (i) and (ii) in MA1.4 hold, vP has not executed(does not stop execution of) activity AVp and either (un1) or (un2) hastaken place (Zeitp t), then:

Mav2.=gmv2*(d1−Nsa/Ns)*1n(1.1+0.2*(bef(vP,MA,t)−cma))*sqrt(1+|rosa(vP,SAVs,t)|);

bef(vP,MA,t):=max(bef(vP,MA,t)−Mav2, cma);des(vP,MA,t):=min(des(vP,MA,t)+1.8*Mav2, 2*(oma−cma));

where 0.007≦gmv2≦0.7 (sugg. gmv2=0.17) and d1 is given in MA1.1.

MA2.1 When (i) OP has executed activity AVsop (time t) which has harmed(or may harm) vP (rosa(vP,AVsop,t)<−3), (ii) vP perceives that OP hasexecuted activity AVsop in order to harm vP, (iii) vP can/couldprevent/diminish the harm of the activity AVsop only in degree 0≦pv≦1,then

Mab3=gm3*(1.1−pv)*1n(1.1+0.2*(bef(vP,MA,t)−cma))*sqrt(1+|rosa(vP,AVsop,t)|);

bef(vP,MA,t):=max(bef(vP,MA,t)−Mab3, cma);des(vP,MA,t):=min(des(vP,MA,t)+2*Mab3, 2*(oma−cma));

where 0.008≦gm3≦0.7 (sugg. gm3=0.17).

MA2.1.1. When conditions (i) and (iii) in MA2.1 hold and, according tovP, OP has not executed activity AVsop in order to harm (has not had theintention to harm) vP then.

bef(vP,MA,t):=max(bef(vP,MA,t)−0.2*Mab3,cma);des(vP,MA,t):=min(des(vP,MA,t)+0.35*Mab3, 2*(oma−cma))

where Mab3 is given in MA2.1.

MA2.2. When (i) vP has executed activity AVsv (time t) in order to harmOP (according to vP, rosa(OP,AVsv,t)<0), (ii) OP can/couldprevent/diminish the harm of the activity AVsv only in degree 0≦ps≦1,then.

Maz3:=gmz3*(0.9−ps)*1n(1.1+0.2*(oma−bef(vP,MA,t)))*sqrt(4+|wpros(OP,AVsv,t)|);

bef(vP,MA,t):=max(min(bef(vP,MA,t)+Maz3, oma), cma);des(vP,MA,t):=max(des(vP,MA,t)−1.4*Maz3, 1);

where 0.008≦gmz3≦0.6 (sugg gmz3=0.12) and vP perceives valuerosa(OP,AVsv,t) as wpros(OP,AVsv,t).

MA3.1 When vP has achieved new situation Sub (time 1) then determinebef(vP,MA,t) and des(vP,MA,t) by the valid pattern ‘(Nma, Nm),eru(vP,MA)=x; . . . ’ in dpm(vP). After this pattern has been applied(s. (4.3)), replace this pattern, in dpm(vP), by ‘((10,10),eru(vP,MA)=−x; where vP leaves Sub)’.

MA3.1.1 When vP leaves situation Sub (ceases to be in situation Sub,time t) and in dpm(vP) is pattern ‘((10,10), eru(vP,MA)=x1; where vPleaves Sub)’ (x1<0) then decrease bef(vP,MA,t) and increase des(vP,MA,t)by the pattern ‘eru(vP,MA)=x1’ as given in (4.3) (where b=MA).

MA3.2. When vP has got in situation Sun (time t) then determinebef(vP,MA,t) and des(vP,MA,t) by the valid pattern ‘(Nma, Nm),eru(vP,MA)=y; . . . ’ in dnm(vP). After this pattern has been applied(s. (4.3)), replace this pattern (in dnm(vP)) by ‘((10,10),eru(vP,MA)=−y; where vP leaves Sun)’.

MA3.2.1. When vP leaves situation Sun (time t) and pattern ‘((10,10),eru(vP,MA)=y1; where vP leaves Sun)’ (y1>0) is in dnm(vP) then increasebef(vP,MA,t) and decrease des(vP,MA,t) by the pattern ‘eru(vP,MA)=y1’ asgiven in (4.3) (where b=MA).

MA3.3 When vP has achieved situation SVub (time t) then:

Maz5.=gmz2*(a1+Nvoa/Nvo)*1n(1.1+0.2*(oma−bef(vP,MA,t)))*sqrt(1+eres(vP,AVop,t));

bef(vP,MA,t).=min(bef(vP,MA,t)+1.4*Maz5, oma);des(vP,MA,t).=max(des(vP,MA,t)−1.5*Maz5, 1);

where gmz2 and eres (here and in MA3 3 1) have the same meaning as inMA1.3.

MA3.3.1. When vP leaves situation SVub (time t) then:

Maz6.=gmz2*(a1+Nvoa/Nvo)*1n(1.1+0.2*(bef(vP,MA,t)−cma))*sqrt(1+eres(vP,AVop,t));

bef(vP,MA,t):=max(bef(vP,MA,t)−1.4*Maz6, cma);des(vP,MA,t).=min(des(vP,MA,t)+1.5*Maz6, 2*(oma−cma))

MA3.4 When vP has got in situation SVun (time t) then.

Mav3.=gmv*(a1+Nsa/Ns)*1n(1.1+0.2*(bef(vP,MA,t)−cma))*sqrt(1+|rosa(vP,AVp,t)|);

bef(vP,MA,t).=max(bef(vP,MA,t)−1.4*Mav3, cma);des(vP,MA,t):=min(des(vP,MA,t)+1.6*Mav3,2*(oma−cma)),

where, here and in MA3.4.1, gmv is given in MA1.4.

MA3.4.1 When vP leaves situation SVun (time t) then

Mav4=gmv*(a1+Nsa/Ns)*1n(1.1+0.2*(oma−bef(vP,MA,t)))*sqrt(1+|rosa(vP,AVp,t)|);

bef(vP,MA,t):=min(bef(vP,MA,t)+1.4*Mav4, oma);des(vP,MA,t)=max(des(vP,MA,t)−1.5*Mav4, 1).

MA4. Time dependent alteration—every d hours execute (with priority 3)the following operations:

bef(vP,MA,t).=min(bef(vP,MA,t)+gmt*1n(1.1+0.05*(1−bef(vP,MA,t)), 0), ifbef(vP,MA,t)<0;

des(vP,MA,t).=max(des(vP,MA,t)−gmt1*1n(1.1+0.2*des(vP,MA,t)), 1);

where gmt and gmt1 depend on vP, 0.004≦gmt≦0.2, 0.005≦gmt1<0.2 (e ggmt=0.014, gmt1=0.02, d=3).

bef(vP,LI,t); LI—need for liking and love. It is a collective notion. LIconsists of needs LI(vP,OSA)—liking and love of vP to OSA—where OSAdenotes an object, a situation or an activity. LI(vP,OSA) is closeconnected with the expectation of vP that OSA increases bef(vP,b,t) orprevents the decrease of bef(vP,b,t), for some needs b.

EXAMPLES:

LI(Fr,Ma)—the love of woman Fr to man Ma. Fr expects that Ma increasesbef(Fr, bi,t) for b1ε{SE,AN,GR, . . . }. Fr expects also that Ma doesnot execute activity which decreases bef(Fr,b1,t), if Fr does not acceptthis decrease. LI(K1,E1)—the love of child Ki to parents E1. Ki expectsthat: (i) the parents E1 increase bef(Ki,bk,t) and prevent an essentialdecrease of these values, for bkε{NU, SN, GR, AN, LE, . . . }, (ii)zulteb(E1,Ki,t) is great and abhas(E1,Ki,t)<1 (the meaning of thesefunctions is given in Sect. 2). LI(E1,Ki)—the love of parents E1 tochild Ki. Ki increases bef(E1,bf,t), for bf in {NA, GR, MA, AN, . . . }E1 expect that zulieb(Ki,E1,t) is great and abhas(Ki,E1,t)<1.

Below, instead of LI(vP,OSA), o1(vP,OSA), c1(vP,OSA), we write in shortLI(OSA), o1(OSA), c1(OSA). It holds−30≦c1(OSA)≦bef(vP,LI(OSA),t)≦o1(vP,OSA)≦30.

In standard situations, bef(vP,LI(OSA),t), o1(OSA), . . . are determinedas follows (except LI1.3, with priority 2):

LI1.1. When (i) vP perceives (time t) that OSA has increasedbef(vP,b,t−tb) by dy(b)>1 or has prevented the decrease ofbef(vP,b,t−tb) by dy(b), in time (t−tb,t), (ii) if OSA is an object thenvP thinks ‘OSA has not been obliged to increase bef(vP,b,t−tb) (toprevent the decrease of bef(vP,b,t−tb) respectively)’, (iii)des(vP,b,t−tb)>3, (iv) bef(vP,LI(OSA),t) and LI(OSA) are not defined,(v) zulteb(vP,OSA,t)>2.5*abhas(vP,OSA,t), abhas(vP,OSA,t)<10,nros(vP,OSA,t)<100 and pros(vP,OSA,t)>2.5*nros(vP,OSA,t), then.

Bd(vP).=Bd(vP)∪{LI(OSA)};bef(vP,LI(OSA),t)=0.2*sqrt(des(vP,b,t-tb)*dy(b));

des(vP,LI(OSA),t):=1; o1(OSA).=bef(vP,LI(OSA),t)+1;c1(OSA):=−0.6*o1(OSA).

LI1.2. When conditions (i), (ii) and (v) in LI1.1 hold, LI(OSA) isdefined and des(vP,b,t−tb)*dy(b)>1 then:

if −8≦o1(OSA)≦20 then po1.=o1(OSA)+8 else if o1(OSA)≧20 then po1=28 elsepo1=0;

Loz.=g1o*1n(31−o1(OSA))*sqrt(des(vP,b,t−tb)*dy(b)*sqrt(po1));

o1(OSA):=min(o1(OSA)+Loz, 30); c1(OSA):=max(c1(OSA)−0.6*Loz, −30);

Lbz.=g1b*1n(1.1+0.2*(o1(OSA))−bef(vP,LI(OSA),t)))*.sqrt(des(vP,b,t−tb)*dy(b)*sqrt(po1));

bef(vP,LI(OSA),t):=min(bef(vP,LI(OSA),t)+Lbz, o1(OSA));

des(vP,LI(OSA),t).=max(des(vP,LI(OSA),t)−1.6*Lbz, 0.6);

where 0.001≦g1o≦0.2, 0.01≦g1b≦0.5 (sugg.g1o=0.008, g1b=0.1).

LI1.3. Every n days execute (with priority 3) the following operations(LI(OSA) is defined).

Lor:=g1r*sqrt(o1(OSA)−c1(OSA));

if o1(OSA)>0 then o1(OSA):=max(o1(OSA)−Lor,0);c1(OSA).=min(c1(OSA)+0.2*Lor, o1(OSA) −1, 0),

bef(vP,LI(OSA),t).=max(min(bef(vP,LI(OSA),t), o1(OSA)), c1(OSA));

des(vP,LI(OSA),t):=max(des(vP,LI(OSA),t)−g1zr*sqrt(o1(OSA)+des(vP,b,t)),0.6);

where 0.0001≦glr≦0.4, 0.0001≦glzr≦0.7, 5≦n≦60 (sugg.: glr=0.02,glzr=0.026, n=30).

Let vPa be a human or artificial creature different from vP.

LI2.1. When bef(vP,LI(vPa),t) is defined and vP has perceived that vPahas refused to increase bef(vP,b,t) or to prevent the decrease ofbef(vP,b,t) by more than 1, although vPa could do it, then:

Loa:=gloa*1n(1+o1(vPa)−c1(vPa))*sqrt(des(vP,b,t));

if −8≦o1(vPa) then o1(vPa)=max(o1(vPa)−Loa, −8);

if −7≦o1(vPa){circumflex over ( )} c1(vPa)<−8 thenc1(vPa):=min(c1(vPa)+0.4*Loa, −8) else if c1(vPa)>o1(vPa)−1 thenc1(vPa)=o1(vPa)−1;

La1:=g1s*1n(1.1+0.2*(bef(vP,LI(vPa),t)−c1(vPa)))*des(vP,b,t);

if −8<o1(vPa) then bef(vP,LI(vPa),t).=max(bef(vP,LI(vPa),t)−La1,c1(vPa)) else bef(vP,LI(vPa),t)=max(min(bef(vP,LI(vPa),t), o1(vPa)),c1(vPa));

des(vP,LI(vPa),t):=max(des(vP,LI(vPa),t)−0.8*La1, 0.6);

where 0.005≦gloa≦0.3, 0.005≦gls≦0.3 (sugg. gloa=0.06, gls=0.05).

LI2.2. When (i) bef(vP,LI(vPa),t) is defined, (ii) vPa increased (beforetime ti) bef(vP,b,t1−tb) by dy1(b,t1)>1 or prevented the decrease ofbef(vP,b,t1−tb) by dy1(b,t1), in time (t1−tb, t2), (iii) at time t>t2,vP has perceived that vPa is not and will not be able to executeactivities which would increase bef(vP,b,t) or prevent the decrease ofbef(vP,b,t), then.

po1=as in LI1.2;Loa1=glo2*1n(1+o1(vPa)−c1(vPa))*sqrt(des(vP,b,t)*dy1(b,t1)*sqrt(po1));

if 1≦o1(vPa) then o1(vPa):=max(o1(vPa)−Loa1, 1);e1(vPa):=min(c1(vPa)+0.5*Loa1, o1(vPa)−1, 0) end;

Lz2=g12*1n(1.1+0.2*(bef(vP,LI(vPa),t)−c1(vPa)))*sqrt(des(vP,b,t)*dy1(b,t1)*sqrt(po1));

bef(vP,LI(vPa),t):=max(bef(vP,LI(vPa),t)−Lz2, c1(vPa));des(vP,LI(vPa),t):=max(des(vP,LI(vPa),t)−1.3*Lz2, 0.6)

where 0.0002≦glo2≦0.02, 0.005−g12≦0.2 (sugg. glo2=0.007, g12=0.06).

LI2.3 Let vP1 be different from vPa. When (i) bef(vP,Ll(vPa), t) isdefined, (ii) vP perceived (in time (t1,t2)) that vPa could increasebef(vP,b,t) only by maximum dym(b)≧0 or could prevent the decrease ofbef(vP,b,t) only by maximum dym(b), where t1<t<t2, (iii) vP hasperceived, in time (t2,t2+tb), that vP1 has increased hef(vP,b,t2) bydy(b,t2)>dym(b) or has prevented by dy(b,t2)>dym(b) the decrease of bef(vP,b,t2), then.

po1=as in LI1.2;Loa2:=glo3*1n(1+o1(vPa)−c1(vPa))*sqrt(des(vP,b,t2)*(dy(b,t2)−dym(b))*sqrt(po1));

if 1≦o1(vPa) then begin o1(vPa).=max(o1(vPa)−Loa2, 1);c1(vPa)=min(c1(vPa)+0.5*Loa2, o1(vPa)−1, 0) end;

bef(vP,LI(vPa), t2+tb).=max(min(bef(vP,LI(vPa), t2+tb), o1(vPa)),c1(vPa));

Lz3.=g13*1n(1.1+0.2*des(vP,LI(vPa),t2+tb))*sqrt(des(vP,b,t2)*(dy(b,t2)−dym(b))*sqrt(po1));

des(vP,LI(vPa),t2+tb):=max(des(vP,LI(vPa), t2+tb)−Lz3, 0.6);

where 0.0004≦glo3≦0.03, 0.005≦g13≦0.2 (sugg:glo3=0.006, g13=0.05)

L13.1. When vP has perceived that vPa intentional decreased or began todecrease bef(vP,b,t−tb) by dy(b)>0.3, or intentional prevented or beganto prevent the increase of bef(vP,b,t−tb) by dy(b,t), in time (t−tb,t)(bef(vP,LI(vPa),t) is defined), then

Lo1.=glo4*1n(1+o1(vPa)−c1(vPa))*sqrt(des(vP,b,t)*dy(b));

if o1(vPa)>−15 then o1(vPa):=max(o1(vPa)−Lo1, −15);c1(vPa).=min(c1(vPa), o1(vPa)−1);

La2=gls2*1n(1.1+0.2*(bef(vP,LI(vPa),t)−c1(vPa)))*sqrt(des(vP,b,t)*dy(b));

bef(vP,LI(vPa),t).=max(bef(vP,LI(vPa),t)−La2, c1(vPa));des(vP,LI(vPa),t).=max(des(vP,LI(vPa),t)−2*La2, 0.6);

where 0.005≦glo4≦0.2, 0.05≦gls2≦0.8 (sugg. glo4=0.06, gls2=0.3).

L13.2 When vP has perceived that vPa decreased or began to decreasebef(vP,b,t−tb) by dy(b)>0.5 not intentional, or prevented or began toprevent the increase of bef(vP,b,t−tb) by dy(b,t) not intentional, intime (t−tb,t) (bef(vP,LI(vPa),t) is defined), then:

if o1(vPa)>−1 then o1(vPa).=max(o1(vPa)−0.2*Lo1, −1);c1(vPa).=min(c1(vPa), o1(vPa) −2, 0),

bef(vP,LI(vPa),t).=max(bef(vP,LI(vPa),t)−0.2*La2, c1(vPa));

des(vP,LI(vPa),t)=max(des(vP,LI(vPa),t)−0.4*La2, 0.6);

where Lo1 and La2 are defined in L13 1

LI4.1. bef(vP,LI(OSA),t) is defined. When (i) vP has perceived (time t)that OSA cannot increase bef(vP,b,t) by dy(b)≧0 or prevent the decreaseof bef(vP,b,t) by dy(b), in time (t,t2), although OSA did it before timet−dt (dt>0), (ii) vP has perceived (time t) that OSA1 (different fromOSA) increases bef(vP,b,t) only by dyl (b,t)<dy(b) or prevents thedecrease of bef(vP,b,t) only by dy(b,t)<dy(b) (if such OSA I does notexist then dyl(bt)=0), then

Ls2=gls3*1n(1.1+0.2*(bef(vP,LI(OSA),t)−c1(OSA)))*sqrt(des(vP,b,t)*(dy(b)−dy1(b,t))*sqrt(po1));

bef(vP,LI(OSA),t):=max(bef(vP,LI(OSA),t)−Ls2, c1(OSA));

des(vP,LI(OSA),t)=min(des(vP,LI(OSA),t)+1.4*Ls2, 2*(o1(OSA)−c1(OSA)));

where po1=as in LI1.2, 0.002≦gls3≦0.2 (sugg. gls3=0.04).

LI4.2. When conditions (i) and (ii) in LI4.1 hold and vP has perceived(time t3>t2) that OSA again increases bef(vP,b,t3) by dy3(b,t3)>dy1(b,t)or prevents the decrease of bef(vP,b,t) by dy3(b,t3), then:

Lz3:=g14*1n(1.1+0.2*(o1(OSA)−bef(vP,LI(OSA),t3)))*sqrt(des(vP,b,t3)*(dy3(b,t3)−dy1(b,t))*sqrt(po1));

bef(vP,LI(OSA),t3):=min(bef(vP,LI(OSA),t3)+Lz3, o1(OSA));

des(vP,LI(OSA),t3).=max(des(vP,LI(OSA),t3)−1.2*Lz3, 1),

where po1=as in LI1.2, 0.002≦g14≦0.2 (sugg g14=0.04).

LI5.1. bef(vP,LI(vPa),t) is defined. When (i) vP expects (in time(t1,t)) that vPa executes activity which will express the intensity ofliking and love of vPa towards vP, zulieb(vPa, vP,t), as value greaterthan ez1(t)≧0, (ii) vP has perceived (time t) that vPa has executedactivity ALe which has expressed value zuliew(vPa,vP,t)≧ez1(t)(zuliew(vPa,vP,t) is the intensity zulieb(vPa,vP,t) as vP it perceives),then:

dze1:=1+zuliew(vPa,vP,t)−ez1(t);Lak1:=gla1*1n(31−o1(vPa))*sqrt(des(vP,LI(vPa),t)*dze1*sqrt(po1));

o1(vPa).=min(o1(vPa)+Lak1, 30); c1(vPa).=max(c1(vPa)−0.4*Lak1, −30);

Lab1=gla2*1n(1.1+0.2*(o1(vPa)−bef(vP,LI(vPa),t)))*sqrt(des(vP,LI(vPa),t)*dze1*sqrt(po1));

bef(vP,L(vPa),t)=min(bef(vP,LI(vPa),t)+Lab1, o1(vPa));

des(vP,LI(vPa),t).=max(des(vP,LI(vPa),t)−1.8*Lab1, 0.6));

where po1=as in LI1.2, 0.0002≦gla1≦0.05, 0.002≦gla2≦0.2 (sugg.gla1=0.005, gla2=0.055).

LI5.2. When condition (i) in LI5.1 holds and vP has perceived that vPahas executed activity ALs which has expressed intensityzuliew(vPa,vP,t)<ez1(t) or vPa has not done such activity (thenzuliew(vPa,vP,t)=0) then:

dald=des(vP,LI(vPa),t)*(ez1(t)−zuliew(vPa,vP,t));Las:=glas*1n(1+o1(vPa)−c1(vPa))*sqrt(dald);

if o1(vPa)>2 then o1(vPa):=max(o1(vPa)−Las, 2);c1(vPa).=min(c1(vPa)+0.2*Las, o1(vPa)−1, 0);

Las1=glas1*1n(1.1+0.2*(bef(vP,LI(vPa),t)−c1(vPa)))*sqrt(dald);

bef(vP,LI(vPa),t):=max(bef(vP,LI(vPa),t)−Las1, c1(vPa));

des(vP,LI(vPa),t).=max(des(vP,LI(vPa),t)−1.2*Las1, 0.6));

where 0.0001≦glas≦0.05, 0.002≦glas1≦0.3 (sugg. glas=0.006, glas1=0.056).

LI5.3 When vP has perceived (time t) that vPa has executed activity Abnwhich has expressed the intensity of dislike and hate of vPa towards vP,abhas(vPa,vP,t), as the value abhaw(vPa,vP,t)>1 (according to vPabhaw(vPa,vP,t) equals abhas(vPa,vP,t)), then:

Lak2:=gla3*1n(1+o1(vPa)−c1(vPa))*sqrt(abhaw(vPa,vP,t)*des(vP,LI(vPa),t));

if o1(vPa)>−5 then o1(vPa):=max(o1(vPa)−Lak2, −5);c1(vPa)=min(c1(vPa)+0.1*Lak2, o1(vPa) −1, 0);

Lab2.=gla4*1n(1.1+0.2*(bef(vP,LI(vPa),t)−c1(vPa)))*sqrt(abhaw(vPa,vP,t)*des(vP,LI(vPa),t));

bef(vPa,LI(vPa),t).=max(bef(vP,LI(vPa),t)−Lab2, c1(vPa));

des(vP,LI(vPa),t):=max(des(vP,LI(vPa),t)−1.8*Lab2, 0.6));

where 0.001≦gla3≦0.2, 0.01≦gla4≦0.8 (sugg. gla3=0.017, gla4=0.15).

LI5.4. bef(vP,LI(vPa),t) is defined. When (i) vP has perceived (time t)that vPa has executed activities which have increased the intensity ofliking and love of vPa towards vP1, zulieb(vPa,vP1,t−t1), by dzu1(vP1,t)and therefore the intensity zuhew(vPa,vP,t) has decreased or willdecrease (according to vP, ziiew(vPa,vP,t) equals zulheb(vPa,vP,t) ),(ii) vP does not accept, in degree 0<an1(vPa,vP1)≦1, the activities ofvPa which cause the increase of zulieb(vPa,vP1,.), then

Lea.=glea*an1(vPa,vP1)*1n(1.1+0.2*(bef(vP,LI(vPa),t)−c1(vPa)))*sqrt(dzu1(vP1,t)*des(vP,LI(vPa),t)*sqrt(po1));

bef(vP,LI(vPa),t).=max(bef(vP,LI(vPa),t)−Lea, c1(vPa));

des(vP,LI(vPa),t):=min(des(vP,LI(vPa),t)+1.8*Lea, 2*(o1(vPa)−c1(vPa));

where po1=as in LI1.2, 0.003≦glea≦0.5 (sugg. glea=0.05).

bef(vP,AN,t); AN—need for recognition, acknowledgment and self-esteem.AN is always connected with a community G of artificial creatures orpeople. Instead of inaccurate need AN, we use AN(G)—need forrecognition, acknowledgment and self-esteem in community G, where G maybe vP. bef(vP,AN(vP),t) and des(vP,AN(vP),t) represent the state ofself-esteem of vP. In general, hef(vP,AN(G) t) changes when vP belongsto community G and one of the following situations occur:

an1. Members of community G execute activities which contain stimuluspatterns of the form ‘ . . . fs(vP,AN(G))= . . . ’;

an2. vP achieves (or loses) higher rank in community G, i e vP achieves(or leaves, respectively) situation which contain patterns of the formgiven in (an1);

an3. vP has accomplished (very good, good, . . . ) threshold values,norms or goals of community G,

an4. vP has executed activity belonging to NRV(G) (NRV(G) is the set ofnorms, principles, rules and behaviours of community G) and members ofcommunity G have done valuation behaviours concerning this activity ofvP.

Also changes of hef(vP,AN(G),t) in cases (an3) and (an4) must bedescribed by stimulus patterns of the form given in (an1), inappropriate situation models and activity schemes.

bef(vP,MB,t); MB—material and financial needs. MB are secondary needs.They realize or support the following primary needs. SN, NU, MA, SE, LE,AN, GR, NE, NA, GE, BZ(Sz). Needs MB are realized by objects which havesale or exchange values. Below, we use the following notations:

z eu Ob-z en of object Oh, where en denotes a measure (e g 1 kg apples,10 ha forest);

u(z en Ob,t)—money value (price) of z en Ob at time t; thus u(z US$money, t)=z US$, u(1 piece house H1, t)=the price of house H1 at time t;

u(AVd, vP,t)—price of activity AVd (a service) when this activity isdone as vP wishes (at time t).

It holds −30≦cmb≦bef(vP,MB t)≦omb≦30.

The initial values: omb=5, cmb=−1, bef(vP,MB,ts)=3, des(vP,MBP,ts)=1.

In standard situations, cmb, omb, bef(vP,MB,t) and des(vP,MB,t) aredetermined (with priority 2) as follows:

MB1.1. When (i) vP has perceived (time t) that eu Ob will increasebef(vP,be,t) by dy(be)>0.3 or prevent the decrease of bef(vP,be,t) bydy(be), in time (t,t1), where des(vP,be,t)*dy(be)>1, for e=1, . . . , n,when vP uses z eu Ob (by activities which vP may execute), (ii) u(z enOb,t) is defined, (iii) vP has got z eu Ob (time t), then

M1.=sqrt(des(vP,b1,t)*dy(b1)+ . . . +des

(vP,bn,t)*dy(bn)); omb.=min(omb+gm1*1n(31−omb)*M1, omb+1, 30);

Mb1.=gmb*1n(1.1+0.2*(omb−bef(vP,MB,t)))*M1;

bef(vP,MB,t):=min(bef(vP,MB,t)+Mb1, omb);des(vP,MB,t).=max(des(vP,MB,t)−1.5*Mb1, 1);

where 0.0002≦gm1≦0.07, 0.01≦gmb≦0.5 (sugg. gm1=0.009, gmb=0.17).

MB1.2 When conditions (i) and (ii) in MB1.1 hold and vP has perceived(time t) that vP can use only z1 EUR<μ(z eu Ob,t)*0.95 for buying z euOb, then.

M2:=sqrt(des(vP,b1,t)*dy(b1)++des(vP,bn,t)*dy(bn))*(1−0.9*(z1 EUR/μ(z euOb,t)));

cmb:=max(cmb−gm2*1n(31+cmb)*M2, cmb−0.7, −30);

Mb2:=gmb2*1n(1.1+0.2*(bef(vP,MB,t)−cmb))*M2;

bef(vP,MB,t):=max(bef(vP,MB,t)−Mb2,cmb);des(vP,MB,t).=min(des(vP,MB,t)+1.8*Mb2, 2*(omb−cmb));

where 0.0002≦gm2≦0.04, 0.02≦gmb2≦0.5 (sugg. gm2=0.006, gmb2=0.13).

Conditions in MB1.1 (and MB1.2) describe a situation, Sm11 (Sm12,respectively). Operations in MB1.1 (MB1.2, respectively) are executedonly then (one time) when vP reaches situation Sm11 (Sm12,respectively).

MB2.1. When (i) vP has separated from object z eu Ob (time t−dt) andcannot get new object z eu Ob, (ii) u(z eu Ob,t) is defined, (iii) vPperceives (time t) that the separation from z eu Ob has decreased orwill decrease bef(vP,be,t−d) by ds(be)>0.3, or has prevented or willprevent the increase of bef(vP,be,t) by ds(be), wheredes(vP,be,t)*ds(be)>1, for e=1, . . . ,n, then:

M21=sqrt(des(vP,b1,t)*ds(b1)+ . . . +des(vP,bn,t)*ds(bn));

cmb=max(cmb−gm21*1n(31+cmb)*M21, cmb−0.7, −30);

Mb3=gmb3*1n(1.1+0.2*(bef(vP,MB,t)−cmb))*M21;

bef(vP,MB,t):=max(bef(vP,MB,t)−Mb3, cmb);

des(vP,MB,t)=min(des(vP,MB,t)+1.8*Mb 3, 2*( omb−cmb));

where 0.0002≦gm21≦0.04, 0.02≦gmb3≦0.5 (sugg. gm21=0.006, gmb3=0.13).

MB2.2. When (i) vP has separated from object z eu Ob (time t1−dt) and vPperceives (in time (t1−dt,t1−0.3*dt)) that this separation (i e notusing z eu Ob) has decreased or will decrease bef(vP,be, t1−dt) byds(be)>0.3, or has prevented or will prevent the increase ofbef(vP,be,1−dt) by ds(be), for e=1 . . . ,u, where des(vP,be,t1−0.3*dt)*ds(be)>1, (ii) vP has got object zl eul Obi (time t, t1≦t≦t1+2*dt) thathas increased bef(vP,ble,t) by dy(ble)>0.3 or has prevented the unwisheddecrease of bef(vP,ble,t) by dy(ble), for e=1, . . . ,n, wheredes(vP,ble,t)*dy(ble)>1, (iii),u(z eu Ob,t1−dt) and u(zl eul Obl,t) aredefined,

(iv) M21=sqrt(des(vP,b1,t1−0.3*dt)*ds(b1)+ . . .+des(vP,bu,t1−0.3*dt)*ds(bu)) and

M22=sqrt(des(vP,b11,t)*dy(b11)+ . . . +des(vP,bln,t)*dy(bln)), then:

(v) if M21<M22 then:

omb:=min(omb+gm1*1n(31−omb)*(M22−M21), omb+1, 30);

Mb22:=gmb1*1n(1.1+0.2*(omb−bef(vP,MB,t)))*(M22−M21);

bef(vP,MB,t):=min(bef(vP,MB,t)+Mb22, omb); des(vP,MB,t)=max(des

(vP,MB,t)−1.5*Mb22, 1);

where gm1 and gmb1 are given in MB1.1;

(vi) if M21>M22 then:

cmb:=max(cmb−gm21*1n(31+cmb)*(M21−M22), cmb−0.7, −30);

Mb23=gmb3*1n(1.1+0.2*(bef(vP,MB,t)−cmb))*(M21−M22);

bef(vP,MB,t).=max(bef(vP,MB,t)−Mb23, cmb);des(vP,MB,t):=min(des(vP,MB,t)+1.8*Mb23, 2*(omb−cmb));

where gm21 and gmb3 are given in MB21.

MB3.1. When (i) vP perceives (time t) that execution of activity AVd (aservice), so as vP wishes, would increase bef(vP,be,t) by dy(be)>0.3 orwould prevent unwished decrease of bef(vP,be,t) by dy(be), in time(t,t1), where

des(vp,be,t)*dy(be)>1, for e=1, . . . ,n, (ii) μ(AVd,vP,t) is defined,(iii) AVd is done as vP wishes, then perform operations given in MB1.1.

MB3.2. When conditions (i) and (ii) in MB3.1 hold and vP perceives (timet) that she/he/it has only z1 EUR<0.95*μ(AVd,vP,t) for the wishedservice AVd, and therefore the service AVd is not done to vP, then(similarly as in MB1.2):

M2:=sqrt(des(vP,b1,t)*dy(b1)+ . . . +des(vP,bn,t)*dy(bn))*(1−0.9*(z1EUR/μ(AVd,vP,t)));

cmb:=max(cmb−gm2*1n(31+cmb)*M2, cmbp−0.7, −30);

Mb2:=gmb2*1n(1.1+0.2*(bef(vP,MB,t)−cmb))*M2;

bef(vP,MB,t):=max(bef(vP,MB,t)−Mb2, cmb);des(vP,MB,t):=min(des(vP,MB,t)+1.8*Mb2, 2*(omb−cmb));

where gm2 and gmb2 are given in MB 1.2.

bef(vP,NA,t); NA—have children. It holds:

cn≦des(vP,NA,t)≦on, 0.1≦on≦30.

Initial values: cn:=−0.7* on; bef(vP,NA,ts):=on−0.05*(on−cn);des(vP,NA,ts).=1.4*(on−bef(vP,NA,ts)).

A new born virtual child, having virtual parents vPm and vPf, may fromthe beginning be grow up. It may gain knowledge and activity models fromthe parents vPm and vPf create (generate) a child when they performcreation behaviour KZV The result of this behaviour is a new virtualcreature vPk who has a match of object, situation and activity modelsand cognition algorithms of vPm and vPf. The parents, however, areauthorized to modify object, situation and activity models and cognitionalgorithms of vPk. vPm and vPf may do activity KZV only if

1≦on, bef(vPx,NA,t)≦on −1, 3.5≦des(vPx,NA,t), for x=‘m’, ‘f’.

NA1. Every 30 days, if des(vP,b,t)<17, for bεBd(vP) and b≢NA, areexecuted following operations:

bef(vP,NA,t):=max (bef(vP,NA,t)−0.5, on);des(vP,NA,t)=min(des(vP,NA,t)+1, 2*(on−cn)).

NA2. If des(vP,b,t)>20 for a need b≢NA and t1≦t≦t2, where t2=t1+15 days,then:

bef(vP,NA,t2):=min(bef(vP,NA,t2)+0.25, on);des(vP,NA,t2):=max(des(vP,NA,t2)−0.5, 0.5).

NA3. When vP has performed creation behaviour KZV and gets child vPk(time t) then:

o1(vPk):=25; c1(vPk):=−15; bef(vP,LI(vPk),t):=o1(vPk)−2;des(vP,LI(vPk),t):=3; on=on−1; cn.=cn+0.5;

bef(vP,NA,t).=on; des(vP,NA,t):=0.5; abhas(vP,vPk,t).=0;zulieb(vP,vPk,t).=500; Fmk:=Fmk∪{vPk};

where Fmk denotes the (community) family of vPk.

bef(vP,BZ(SMsz),t); BZ(SMsz)—achieve goal situation (of vP) SMsz, wherevP has the model SMsz. BZ(SMsz) is a secondary need vP has decided (timet1), by her/his/its cognition algorithms (e g motivation and controlprocedures ), that situation SMvz is her/his/its goal which vP will toachieve until time t2.Let pz(I) be the probability of achieving goalsituation SMsz, as vP estimates at time t. pz(t) expresses the hope ofvP (at time t) of achieving the goal SMsz. SMvz is added to list LZS ofgoal situations as follows (s. Schurmann [AS1] (2000), Sect. 3.2):

(4.4) (SMsz, pz(t), t1, t2; dsE1, . . . , dsEu; AV1, . . . , AVw; . . .)

where dsEt denotes a new stimulus pattern of the goal SMsz (added to thelist in time (t1,t)) and AVe (e≦w) are activities by which vP planes toachieve the goal SMsz. dsEi has one of the two forms

(‘dser’; ((Na,N), fs(vP,b)=). . . )−vP expects (with probability Na/N)that, (immediately) after vP achieves the goal SMsz, bef(vP,b,.) anddes(vP,b,.) will change according to the patternfv(vP,b)= . . . (in mostcases,fs is here a pattern that increases bef(vP,b,.));

(‘dsne’; ((Na,N), fs(vP,b)=). . . )−vP expects (with probability Na/N)that, when vP does not achieve goal situation SMsz, bef(vP,b,.) anddes(vP,b,.) will change according to the pattern fs(vP,b)= . . . (inmost cases, fs is here a pattern that decreases bef(vP,b,.))

Let:

Bp=|{ . . . fsp(vP,b)=) is in SMsz or (‘dser’; ( . . . fsp(vP,b)= . . .)) is attached=to situation SMsz, in the list LZS}, wherefsp is name ofa positive stimulus pattern (epb, epbu, upb, upbu, vtzb, eru iferu(vP,b)=x and x>0),

Bn={b|( . . . fsn(P,b)=) is in SMfsz or (‘dser’; ( . . . fsn(vP,b)= . .. )) is attached to SMsz in the list LZS},

where fsn is name of a negative stimulus pattern (enb, enbu, unb, unbu,vpb, eru if eru(vP,b)=y and y<0),

Dp={b|(‘dsne’; ( . . . fvp(vP,b)= . . . )) is attached to SMsz in LZS},

Dn={b|(‘dsne’; ( . . . fsn(vP,b)= . . . )) is attached to SMsz in LZS}

The intensity of stimulus of goal situation SMvz (at time t), if vP)would achieve goal situation SMsz, equals:

rzse(vP,SMsz,t)=Σ_(bεBp) epr(vP,fsp,b,a,t)−Σ_(beBn) enr(vP,fsn,b,a,t)

(the meaning of epr and enr is given in Sect. 2).

The intensity of stimulus of goal situation SMsz (at time t), if vPwould not achieve goal situation SMsz, equals:

rzsn(vP,SMsz,t)=Σ_(bεDp) epr(vP,fsp,b,a,t)−Σ_(bεDn) enr(vPf,sn,b,a,t)

Below, instead of SMsz, we write in short Sz. It holds

cz(Sz)≦bef(vP,BZ(Sz),t)≦oz(Sz).

bef(vP,BZ(Sz),t) and des(vP,BZ(Sz),t) are determined by the followingrules (with priority 2)

BZ1. When cognition algorithms of vP have decided/concluded that Sz isgoal situation (time t, Sz is added to the list LZS) then

Bd(vP)=Bd(vP)∪{BZ(Sz)}; oz(Sz).=min(gzo*sqrt(rzse(vP,Sz,t)*pz(t)), 18);cz(Sz)=−0.7*oz(Sz);

bef(vP,BZ(Sz),t)=oz(Sz)−0.9*(oz(Sz)−cz(Sz));des(vP,BZ(Sz),t):=1.6*(oz(Sz)−cz(Sz));

where pz(t) has the same meaning as in the list LZS and 0.04≦gzo≦0.9(sugg. gzo=0.38).

BZ2.1 When vP has achieved intermediate goal zSz of the goal Sz (time t)and vP thinks that the probability pz(t) of achieving the goal Sz haschanged by dpz(t)=pz(t)−pz(ta) (where pz(ta) is the probability attachedto Sz in the list LZS) then.

i. if dpz(t)≧0 then:

efr.=sqrt(dpz(t)*rzse(vP,Sz,t)); oz(Sz):=min(oz(Sz)+gzo1*efr, 30);cz(Sz)=max(cz(Sz)−0.6*gzo1*efr, −28);

oz1=oz(Sz)−0.2*(oz(Sz)−cz(Sz));Lz1=gz1*efr*1n(1.1+0.2*(oz1−bef(vP,BZ(Sz),t)));

bef(vP,BZ(Sz),t):=min(bef(vP,BZ(Sz),t)+Lz1, oz1);

des(vP,BZ(Sz),t).=max(des(vP,BZ(Sz),t)−1.2*Lz1, 0.3*(oz(Sz)−cz(Sz));

ii. if dpz(t)<0 then:

efr.=sqrt(−dpz(t)*rzse(vP,Sz,t)); oz(Sz):=max(oz(Sz)−gzo1*efr,cz(Sz)+1);

cz(Sz):=min(cz(Sz)+0.5*gzo1*efr, oz(Sz)−1);

Lz1.=gz1*efr*1n(1.1+0.2*(bef(vP,BZ(Sz),t)−cz(Sz)));bef(vP,BZ(Sz),t):=max(bef(vP,BZ(Sz),t)−Lz1, cz(Sz));

des(vP,BZ(Sz),t):=min(des(vP,BZ(Sz),t)+1.3*Lz1, 1.8*(oz(Sz)−cz(Sz));

where 0.001≦gzo1≦0.4, 0.02≦gz1≦0.8 (sugg. gzo1=0.03, gz1:=0.18).

BZ2.2. When vP has achieved goal situation Sz (time t) then (delete Szin the list LZS):

Bd(vP).=Bd(vP)−{BZ(Sz)}; bef(vP,BZ(Sz),t):=oz(Sz); des(vP,BZ(Sz),t)=0.

BZ2.3. When (i) vP executes activities in order to achieve goal Sz, intime (t−xt, t), (ii) in this time vP has perceived an obstacle whichprevents, in degree 0<gv≦1, the achieving of the goal Sz (if gv=1 then,according to vP, he/she/it cannot achieve Sz), then

efr=sqrt(gv*pz(t−xt)*rzse(vP,Sz,t)); Lz2.=gz2*efr;oz(Sz):=max(oz(Sz)−gzo2*efr, cz(Sz)+1);

cz(Sz):=min(cz(Sz)+0.4*gzo2*efr, oz(Sz)−1);bef(vP,BZ(Sz),t):=max(bef(vP,BZ(Sz),t)−Lz2, cz(Sz));

des(vP,BZ(Sz),t):=min(des(vP,BZ(Sz),t)+1.2*Lz2, 1.8*(oz(Sz)−cz(Sz));

where 0.05≦gzo2≦0.7, 0.1≦gz2≦2 (sugg. gzo2=0.13, gz2=0.28).

BZ2.4. When (i) vP believed (at time t−xt) that she/he/it would achievegoal Sz, with probability pz(t−xt), (ii) vP has perceived, in time(t−xt,t), that vP does not or cannot achieve the goal Sz, then.

efr1:−sqrt(pz(t−xt))*(sqrt(rzse(vP,Sz,t))−sign(rzsn(vP,Sz,t))*sqrt(|rzsn(vP,Sz,t)|);

oz(Sz).=max(oz(Sz)−gzo3*efr1, cz(Sz)+1);cz(Sz):=min(cz(Sz)+0.3*gzo3*efr1, oz(Sz)−1); Lz3:=gz3*efr1;

bef(vP,BZ(Sz),t):=max(bef(vP,BZ(Sz),t)−Lz3, cz(Sz));

des(vP,BZ(Sz),t).=min(des(vP,BZ(Sz),t)+Lz3, 1.8*(oz(Sz)−cz(Sz));

where 0.04≦gzo3≦0.7, 0.5≦gz3≦4 (sugg. gzo3=0.14, gz3=0.5).

Afterwards, if oz(Sz−cz(Sz)>1, bef(vP,BZ(Sz),.) and des(vP,BZ(Sz),.)change every n days as follows:

Loz.=gzr*sqrt(oz(Sz)−cz(Sz)); oz(Sz):=oz(Sz)−Loz;cz(Sz):=min(cz(Sz)+0.4*Loz, oz(Sz)−0.6);

bef(vP,BZ(Sz),t):=max(min(bef(vP,BZ(Sz),t), oz(Sz), cz(Sz));des(vP,BZ(Sz),t).=max(des(vP,BZ(Sz),t)−3.8*Loz, 1)

where 0.001≦gzr≦0.4, 1≦n≦60 (sugg. gzr=0.07, n=10)

bef(vP,BH(OK),t), BH(OK)—help object OK if it needs help, where OKdenotes an artificial creature, a living object or a group of artificialcreatures or living objects. It holds

−18≦mch(vP)−ch(OK)≦bef(vP,BH(OK),t)≦oh(OK)≦moh(vP)≦25.

Below, instead of mch(vP) and moh(vP), we write in short mch und moh. Ifmoh−mh≦2 then vP has not the need BH. Let. mch≦0, moh>2;

zulb(vP,OK,t)=zulieb(vP,OK,t) if zulieb(vP,OK,t) is defined,zulb(vP,OK,t)=0 otherwise;

abh(vP,OK,t)=abhas(vP,OK,t) if abhas(vP,OK,t) is defined, ahh(vP,OK,t)=0otherwise;

zfw(OK,b,t)=the value zful(OK,b,t) as vP perceives it, e g vP believesthat zfw (H,SN,t)=−20 although person H perceives that zful(H,SN,t)=1(the meanings of zulieb, abhas and zful are given in Sect 2);

dsw(OK,b,t)=the value des(OK,b,t) as vP perceives it;

WA={b|dsw(OK,b,t)>0.33*max(dsw(OK,b1,t), for b1εBd(OK))};

WP={b|des(vP,b,t)>0.33*max(des(vP,b1,t), for b1εBd(vP))},

unku(vP,t)=Σ_(bεWP) zful(vP,b,t); ukw(OK,b,t)=Σ_(bεWA) zfw(OK,b,t);

shu(OK,t)=(sc1(vP)+sqrt(abh(vP,OK,t))/(sqrt(z1b(vP,OK,t)+5), if OK isnot child of vP=−20, if OK is child of vP, where 5≦sc1(vP)≦800 (suggsc1(vP)=90);

unfs(vP,OK,t)=unku(vP,t)−ukw(OK,t)−shu(OK,t) (if unfs(vP,OK,t)<0 then vPhas not the need to help OK);

z1h(vP,OK,t)=zu1b(vP,OK,t)−sc2(vP)*abh(vP,OK,t)+1, where 0.2≦sc2(vP)≦2(if z1h(vP,OK,t)<0 then vP has not the need to help OK).

BH1. When (i) unfs(vP,OK,t)>0{circumflex over ()}z1h(vP,OK,t)>0{circumflex over ( )} ukw(OK,t)<0, (ii) vPperceives/believes (time t) that OK cannot do any activity which wouldincrease ukw(OK,t), then.

iii. if bef(vP,BH(OK),t) is not defined:

Bd(vP).=Bd(vP)∪{BH(OK)}; d4=0.2;

oh(OK)=min(gho*sqrt(unfs(vP,OK,t)*z1h(vP,OK,t)), 0.8*moh);ch(OK).=max(0.6*mch, −0.5*oh(OK));

bef(vP,BH(OK),t).=oh(OK)−0.6*(oh(OK)−ch(OK));des(vP,BH(OK),t)=oh(OK)−ch(OK));

where 0.004≦gho≦0.4 (sugg. gho=0.08);

iv. if bef(vP,BH(OK),t) is defined.

Lho.=gho1*sqrt(unfs(vP,OK,t)*z1h(vP,OK,t)); ch(OK):=max(ch(OK)−Lho,ch(OK)−d4*(ch(OK)−mch));

Lh1.=gh1*1n(1.1+0.2*(bef(vP,BH(OK),t)−ch(OK)))*sqrt(unfs(vP,OK,t)*z1h(vP,OK,t));

bef(vP,BH(OK),t).=max(bef(vP,BH(OK),t)−Lh1,ch(OK));

des(vP,BH(OK),t).=min(des(vP,BH(OK),t)+2.3*Lh1, 2*(oh(OK)−ch(OK));

where 0.0005≦gho1≦0.1, 0.005≦gh1≦0.2 (sugg. gho1=0.01, gh1=0.04).

BH2.1 When (i) bef(vP,BH(OK),t−xt) is defined, (ii)unfs(vP,OK,t−xt)>0{circumflex over ( )}z1h(vP,OK,t−xt)>0 {circumflexover ( )}ukw(OK,t−xt)<0,

(iii) vP perceived/believed (time t−xt) that OK could not do anyactivity in order to increase ukw(OK, t−xt), (iv) vP has executedactivities which have increased ukw(OK,t−xt) by dzw, in time (t−xt,t),then:

Lho1:=gho2*sqrt((1+d4*(moh−oh(OK)))*z1h(vP,OK,t−xt))*dzw/unfs(vP,OK,t−xt);

oh(OK).=min(oh(OK)+Lho1, oh(OK)+d4*(moh−oh(OK)));

bef(vP,BH(OK),t):=min(bef(vP,BH(OK),t)+gh2*sqrt((oh(OK)−bef(vP,BH(OK),t))*z1h(vP,OK,t−xt))*dzw/unfs(vP,OK,t−xt),oh(OK));

des(vP,BH(OK),t)=max(des(vP,BH(OK),t)*ghs*(1−dzw/unfs(vP,OK,t−xt)),0.3);

where 0.005≦gho2≦0.9, 0.02≦gh2≦0.9, 0.02≦ghs≦1.2 (sugg gho2=0.1,gh2=0.3, ghs=0.25).

BH2.2. When (i) conditions (i), (ii) and (iii) in BH2 1held, (ii) vP didnot execute activity to increase ukw(OK,t−xt),

(iii) vP perceives (time t) that other vPa did activities which haveincreased ukw(OK,t−xt) by dzw, then.

bef(vP,BH(OK),t)=min(bef(vP,BH(OK),t)+0.4*gh2*sqrt((oh(OK)−bef(vP,BH(OK),t))*z1h(vP,OK,t−xt))*dzw/unfs(vP,OK,t−xt), oh(OK));

des(vP,BH(OK),t).=max(0.5*(oh(OK)−bef(vP,BH(OK),t)),0.3).

BH2.3. When (i) conditions (i), (ii), (iii) and (iv) in BH2.1 held, (ii)at time t+zt, vP perceives that the value ukw(OK,t−xt) was false andthat the real value of ukw(OK,t−xt) was equal to rukw(OK,t−xt) andrukw(OK,t−xt) ukw(OK,t−xt)>2, then:

rkw.=sqrt(rukw(OK,t−xt)−ukw(OK,t−xt)); shu(OK)=shu(OK)+5+rkw;och:=oh(OK)−ch(OK);

Lho2=gho3*1n(1+0.1*och)*rkw; oh(OK).=max(oh(OK)−Lho2, oh(OK)−0.35*och);

ch(OK)=min(ch(OK)+0.7*Lho2, ch(OK)+0.25*och);

bef(v,P,BH(OK),t+zt):=max(min(bef(vP,BH(OK),t+zt), oh(OK)), ch(OK));des(vP,BH(OK),t).=0.3;

where 0.01≦gho3≦1 (sugg. gho3=0.3).

BH3. When (i) vP executed in the past (time (t1−xt,t1)) activities inorder to increase ukw(OK,t1−xt) (bef(vP,BH(OK), t1) was determined),(ii) now (time t), OK has executed activities which have caused harm tovP (i.e. decreased bef(vP,b,t−at) or prevented the increase ofbef(vP,b,t−at), for some needs b), then:

d4:=d4*0.85; shu(OK).=shu(OK)+30; och:=oh(OK)−ch(OK);

oh(OK):=oh(OK)−0.45*och; ch(OK)=ch(OK)+0.35*och;

bef(vP,BH(OK),t):=max(min(bef(vP,BH(OK),t), oh(OK)), ch(OK));des(vP,BH(OK),t).=0.

References

[AS1] Schurmann A. Darstellung von Emotionen in Elektronischen Geräten;international patent application submitted to Deutsches Patent- undMarkenamt (German Patent and Trade Mark Office) in Munchen,international application No. PCT/DE00/03210; international filing dateSep. 14, 2000; English translation (“Representation of Emotions inElectronic Devices”) is submitted to the U K Patent Office, ApplicationNo. GB 0204181.2.

[AS2] Schurmann A.: An Idea how to Define Semantics for a Simple NaturalLanguage; (48 pages), 1999, the paper may be send by the author.

[AS3] Schurmann A.: A Simple Thinking Artificial Servant; (48 pages),1998; the paper may be send by the author.

[AS4] Schurmann A. Cooperation in a Motivated, Behaviour BasedMulti-Agent System; (16 pages), 1998; the paper may be send by theauthor.

What is claimed is:
 1. A method for calculating intensities ofsatisfactions and desires, or tensions of needs, of a virtual creaturesuch as motivated agent system, virtual human in entertainment softwareor in Internet, denoted by vP, where this system and method can beembodied/implemented in said creature vP, comprising: determiningintensities of satisfaction and desire of said vP with respect to theneeds for attention and identification, determining intensities ofsatisfaction and desire of said vP with respect to curiosity and theneed for knowledge, determining intensities of satisfaction and desireof said vP with respect to the need for belonging to a community,determining intensities of satisfaction and desire of said vP withrespect to the need for power over people and animals, determiningintensities of satisfaction and desire of said vP with respect to theneed for liking and love, determining intensities of satisfaction anddesire of said vP with respect to material and financial needs,determining intensities of satisfaction and desire of said vP withrespect to the need to achieve goal situation, determining intensitiesof satisfaction and desire of said vP with respect to the need to havechildren, determining intensities of satisfaction and desire of said vPwith respect to the need to help a living object and calculatingintensities of satisfactions and desires of said vP by stimulus patternsconnected with perceived objects, situations and activities.
 2. A methodaccording to claim 1, wherein determining intensities of satisfactionand desire of the virtual creature vP of with respect to the needs forattention and identification comprising: a) function values bef(vP,AU,t)and des(vP,AU,t) representing the current intensities of satisfactionand desire, respectively, of said virtual creature vP (at present timet) with regard to its ground attention (denoted by AU); softwareprocedure which determines said intensities of satisfaction bef(vP,AU,t)and desire des(vP,AU,t) of said creature vP with regard to said groundattention AU, every given period of time (e.g. every 2 min), when vP isactive, wherein said procedure use the last value of said satisfactionbef(vP,AU,t) and the current intensity of desire for relaxation of saidvP; b) function values bef(vP,AUw(OS),t) and des(vP,AUw(OS),t)representing the current intensities of satisfaction and desire,respectively, of said virtual creature vP (at present time t) withregard to the need for attention and identification with respect to anobject or a situation, denoted by OS, (this need is denoted by AUw(OS));software procedures which determine said intensities of satisfactionbef(vP,AUw(OS),t) and desire des(vP,AUw(OS),t) of said vP with respectto said need for attention and identification AUw(OS), when said vP isidentifying said object or situation OS, wherein said procedures usesaid current values of satisfaction bef(vP,AU,t) and desire des(vP,AU,t)and current intensities of positive stimulus and negative stimulus ofsaid object or situation OS, where said vP has an object or situationmodel of said OS and said intensities of positive and negative stimuliare calculated by stimulus patterns connected with said model of OS; c)function values bef(vP,AUa(AV),t) and des(vP,AUa(AV),t) representing thecurrent intensities of satisfaction and desire, respectively, of saidvirtual creature vP (at Present time t) with regard to the need forattention and identification with respect to an activity, denoted by AV,(this need is denoted by AUa(AV)); and software procedures whichdetermine said intensities of satisfaction bef(vP,AUa(AV),t) and desiredes(vP,AUa(AV),t) of said vP with respect to said need for attention andidentification AUa(AV), when said vP is executing said activity AV,wherein said procedures use said current values of satisfactionbef(vP,AU,t) and desire des(vP,AU,t), and current intensities ofpositive stimulus and negative stimulus of the current executed (by vP)sub-activity of said activity AV, where vP has a model (or a scheme) ofsaid activity AV and said intensities of positive and negative stimuliare calculated by stimulus patterns connected with said model ofactivity AV.
 3. A method according to claim 1, wherein determiningintensities of satisfaction and desire of the virtual creature vP ofwith regard to curiosity and the need for knowledge comprising: a)function values bef(vP,NEw(OS),t) and des(vP,NEw(Os),t) representing thecurrent intensities of satisfaction and desire, respectively, of saidvirtual creature vP (at present time t) with regard to curiosity and theneed for knowledge with respect to an object or a situation, denoted byOS, (this need is denoted by NEw(OS)), where said satisfaction valuebef(vP,NEw(OS),t) is greater than or equal to a changing limit value,denoted by cnw(OS), and less than or equal to a changing limit value,denoted by onw(OS); software procedures which determine said intensitiesof satisfaction bef(vP,NEw(OS),t) and desire des(vP,NEw(OS),t) of saidcreature vP with regard to said curiosity and the need for knowledgeNEw(OS), when said vP perceives said object or situation OS, whereinsaid procedures use the last and the current said limit values cnw(OS)and onw(OS), and the last values of said satisfaction bef(vP,NEw(OS),t)and desire des(vP,NEw(OS),t); b) function values bef(vP,NEk(OSM),t) anddes(vP,NEk(OSM),t) representing the current intensities of satisfactionand desire, respectively, of said virtual creature vP (at present timet) with regard to the need for knowledge of an object or situationmodel, denoted by OSM, (this need is denoted by NEk(OSM)), where saidsatisfaction value bef(vP,NEk(OSM),t) is greater than or equal to achanging limit value, denoted by cnk(OSM), and less than or equal to achanging limit value, denoted by onk(OSM); software procedures whichdetermine said intensities of satisfaction bef(vP,NEk(OSM),t) and desiredes(vP,NEk(OSM),t) of said creature vP with regard to said need NEk(OSM)for knowledge of said object or situation model OSM, when vP has appliedits own cognition algorithm to said model OSM, wherein said proceduresuse the last and the current said limit values cnk(OSM) and onk(OSM),and the last values of said satisfaction bef(vP,NEk(OSM),t) and desiredes(vP,NEk(OSM),t); c) function values bef(vP,NEz(SM),t) anddes(vP,NEz(SM),t) representing the current intensities of satisfactionand desire, respectively, of said creature vP (at present time t) withregard to the need “how can a goal situation, denoted by SM, beachieved” (this need is denoted by NEz(SM)), where said satisfactionvalue bef(vP,NEz(SM),t) is greater than or equal to a changing limitvalue, denoted by cnz(SM), and less than or equal to a changing limitvalue, denoted by onz(SM); and software procedures which determine saidintensities of satisfaction bef(vP,NEz(SM),t) and desiredes(vP,NEz(SM),t) of said vP with regard to said need NEz(SM) “how cangoal situation SM be achieved”, when vP builds a new activity model (orscheme) such that its execution should lead to said situation SM,wherein said procedures use the last and the current said limit valuescnz(SM) and onz(SM), and the last values of said satisfactionbef(vP,NEz(SM),t) and desire des(vP,NEz(SM),t).
 4. A method according toclaim 1, wherein determining intensities of satisfaction and desire ofthe virtual creature vP of with regard to the need for belonging to acommunity comprising: function values bef(vP,GR(G),t) anddes(vP,GR(G),t) representing the current intensities of satisfaction anddesire, respectively, of said virtual creature vP (at present time t)with regard to the need for belonging to a community, denoted by G,(this need is denoted by GR(G)), where said satisfaction valuebef(vP,GR(G),t) is greater than or equal to a changing limit value,denoted by cgr(G), and less than or equal to a changing limit value,denoted by ogr(G), and software procedures which determine saidintensities of satisfaction bef(vP,GR(G),t) and desire des(vP,GR(G),t)of said creature vP with regard to said need GR(G) for belonging tocommunity G, when said vP executes or cannot execute an activity(denoted by AVv) with members of said community G, or members of saidcommunity G execute an activity (denoted by AVgr), such that saidactivity AVv or AVgr, respectively, changes some intensities ofsatisfactions of vP, denoted by bef(vP,b,t), by some values dy(b), forsome needs b of vP, wherein said procedures use the last and the currentsaid limit values cgr(G) and ogr(G), and the last said values ofsatisfaction bef(vP,GR(G),t) and desire des(vP,GR(G),t), and said valuesdy(b).
 5. A method according to claim 1, wherein determining intensitiesof satisfaction and desire of the virtual creature vP of with regard tothe need for power over people and animals comprising: function valuesbef(vP,MA,t) and des(vP,MA,t) representing the current intensities ofsatisfaction and desire, respectively, of said virtual creature vP (atpresent time t) with regard to the need for power over people andanimals (this need is denoted by MA), where said satisfaction valuebef(vP,MA,t) is greater than or equal to a changing limit value, denotedby cma, and less than or equal to a changing limit value, denoted byoma, and software procedures which determine said intensities ofsatisfaction bef(vP,MA,t) and desire des(vP,MA,t) of said creature vPwith regard to said need MA for power over people and animals, when vPis in one of the three situations: (sm1) said vP ordered an artificialcreature or a virtual organization (denoted by OP) to execute or to stopan activity (denoted by AVo), or said vP has executed an activity(denoted by AVv) to cause said OP to execute or to stop an activity AVo,(sm2) said vP has made said OP a harm and OP has to bear it, (sm3) saidvP gets more power or vP loses power,  wherein said procedures use thelast and the current said limit values cma and oma, and the last valuesof said satisfaction bef(vP,MA,t) and desire des(vP,MA,t), and, if vP isin said situation (sm1) or (sm2), intensities of stimuli of saidactivities AVv and AVo as they are perceived by vP.
 6. A methodaccording to claim 1, wherein determining intensities of satisfactionand desire of the virtual creature vP of with regard to the need forliking and love comprising: function values bef(vP,LI(OSA),t) anddes(vP,LI(OSA),t) representing the current intensities of satisfactionand desire, respectively, of said creature vP (at present time t) withregard to the need for liking and love to an object, a situation or anactivity (this need is denoted by LI(OSA), where OSA denotes saidobject, situation or activity), where said satisfaction valuebef(vP,LI(OSA),t) is greater than or equal to a changing limit value,denoted by cl(OSA), and less than or equal to a changing limit value,denoted by ol(OSA), and software procedures which determine saidintensities of satisfaction bef(vP,LI(OSA),t) and desiredes(vP,LI(OSA),t) of said vP with regard to said need LI(OSA) for likingand love to said OSA, when vP is in one of the following situations:(s1) said OSA has increased (or refuses to increase) some satisfactionvalues of vP, denoted by bef(vP,b,t), by some values dy(b)>0 or hasprevented (or refuses to prevent) a decrease of some satisfaction valuesof vP, denoted by bef(vP,b,t), by some values dy(b), for some needs b ofvP, (s2) said OSA has decreased (or will decrease) some satisfactionvalues of vP, denoted by bef(vP,b,t), by some values dy(b)>0 or hasprevented (or will prevent) an increase of some satisfaction values ofvP, denoted by bef(vP,b,t), by some values dy(b), for some needs b ofvp,  wherein said procedures use the last and the current said limitvalues cl(OSA) ol(OSA), and the last values of said satisfactionbef(vP,LI(OSA),t) and desire des(vP,LI(OSA),t), and said values dy(b).7. A method according to claim 1, wherein determining intensities ofsatisfaction and desire of the virtual creature vP of with regard tomaterial and financial needs (denoted by MB), comprising: functionvalues bef(vP,MB,t) and des(vP,MB,t) representing the currentintensities of satisfaction and desire, respectively, of said virtualcreature vP (at present time t) with regard to said material andfinancial needs MB, and software procedures which determine saidintensities of satisfaction bef(vP,MB),t) and desire des(vP,MB,t) ofsaid vP with regard to said material and financial needs MB, when saidvP achieves one of the following situations: (s1) vP perceives that anobject or an activity done to vP would increase some satisfaction valuesof vP, denoted by bef(vP,b,t), by some values dy(b)>0 or would prevent adecrease of some satisfaction values of vP, denoted by bef(vP,b,t), bysome values dy(b), for some needs b of vP, where said object andactivity have money values, (s2) vP has separated from an object whichhas money value and perceives that therefore some satisfaction values ofvP, denoted by bef(vP,b,t), have decreased (or will decrease) by somevalues dy(b)>0, for some needs b of vP,  wherein said procedures use thelast values of said satisfaction bef(vP,MB,t) and desire des(vP,MB,t)and said values dy(b).
 8. A method according to claim 1, whereindetermining intensities of satisfaction and desire of the virtualcreature vP of with regard to the need to have children comprising:function values bef(vP,NA,t) and des(vP,NA,t) representing the currentintensities of satisfaction and desire, respectively, of said creaturevP (at present time t) with regard to the need to have children (thisneed is denoted by NA), where said satisfaction value bef(vP,NA,t) isgreater than or equal to a changing limit value, denoted by cn, and lessthan or equal to a changing limit value, denoted by on, and softwareprocedures which determine said intensities of satisfaction bef(vP,NA,t)and desire des(vP,NA,t) of said vP with regard to said need NA to havechildren, wherein said procedures use the current said limit values cnand on, and the last values of said satiisfaction bef(vP,NA,t) anddesire des(vP,NA,t).
 9. A method according to claim 1, whereindetermining intensities of satisfaction and desire of the virtualcreature vP of with regard to the need to achieve a goal situation of vPcomprising: function values bef(vP,BZ(Sz),t) and des(vP,BZ(Sz),t)representing the current intensities of satisfaction and desire,respectively, of said virtual creature vP (at present time t) withregard to the need to achieve a goal situation of vP (this need isdenoted by BZ(Sz), where Sz denotes said goal situation), where saidsatisfaction value bef(vP,BZ(Sz),t is greater than or equal to achanging limit value, denoted by cz(Sz), and less than or equal to achanging limit value, denoted by oz(Sz), and software procedures whichdetermine said intensities of satisfaction bef(vP,BZ(Sz),t) and desiredes(vP,BZ(Sz),t) of said vP with regard to said need BZ(Sz) to achievesaid goal situation Sz, when some cognition algorithms of vP haveconcluded that situation Sz should be a goal and when vP is trying toachieve said situation Sz, wherein said procedures use the last and thecurrent said limit values cz(Sz) and oz(Sz), and the last values of saidsatisfaction bef(vP,BZ(Sz),t) and desire des(vP,BZ(Sz),t), and thecurrent intensity of stimulus of said goal situation Sz.
 10. A methodaccording to claim 1, wherein determining intensities of satisfactionand desire of the virtual creature vP of with regard to the need to helpa living object (a real or virtual human, animal or plant) when thisliving object needs help, comprising: function values bef(vP,BH(OK),t)and des(vP,BH(OK),t) representing the current intensities ofsatisfaction and desire, respectively, of said virtual creature vP (atpresent time t) with regard to said need to help said living object(this need is denoted by BH(OK), where OK denotes said living object),where said satisfaction value bef(vP,BH(OK),t) is greater than or equalto a changing limit value, denoted by ch(OK), and less than or equal toa changing limit value, denoted by oh(OK), and software procedures whichdetermine said intensities of satisfaction bef(vP,BH(OK),t) and desiredes(vP,BH(OK),t) of said vP with regard to said need BH(0K) to help saidliving object OK, wherein said procedures use: (a) the last and thecurrent said limit values ch(OK) and oh(OK), (b) the last values of saidsatisfaction bef(vP,BH(OK),t) and desire des(vP,BH(OK),t), (c) thecurrent intensity of liking and love of vP to said object OK and thecurrent intensity of dislike and hate of vP to said object OK, (d) andthe current intensities of contentment and joy or dissatisfaction, griefand suffering, with respect to needs of vP.
 11. A method according toclaim 1, wherein calculating intensities of satisfactions and desires ofthe virtual creature vP of by stimulus patterns comprising: stimuluspatterns with regard to such needs of said creature vP as: be healthy,have no pain, be alive, be in normal environment, the need for visualbeauty (let these needs be denoted by bo), where these stimulus patternsare connected with object, situation and activity models of said vP;function values bef(vP,bo,tp) and des(vP,bo,tp) representing theintensities of satisfactions and desires, respectively, of said creaturevP (at time tp, where t≦tp≦t2 and t is the present time, and t2−t≦1month) with regard to said needs bo; and software procedures whichcalculate said intensities of satisfactions bef(vP,bo,tp) and desiresdes(vP,bo,tp) of said vP with regard to said time tp≦t and to each saidneed bo which occurs in a stimulus pattern of the model (denoted byMOSA) of the object or the situation or the activity which said vP isperceiving (at time t), wherein said procedures calculate saidintensities of satisfactions bef(vP,bo,tp) and desires des(vP,bo,tp) bysaid stimulus patterns (with respect to said needs bo) which areconnected with said model MOSA.